Trace and contextual fear conditioning require neural activity and NMDA receptor-dependent transmission in the medial prefrontal cortex

The contribution of the medial prefrontal cortex (mPFC) to the formation of memory is a subject of considerable recent interest. Notably, the mechanisms supporting memory acquisition in this structure are poorly understood. The mPFC has been implicated in the acquisition of trace fear conditioning, a task that requires the association of a conditional stimulus (CS) and an aversive unconditional stimulus (UCS) across a temporal gap. In both rat and human subjects, frontal regions show increased activity during the trace interval separating the CS and UCS. We investigated the contribution of prefrontal neural activity in the rat to the acquisition of trace fear conditioning using microinfusions of the γ-aminobutyric acid type A (GABA_A) receptor agonist muscimol. We also investigated the role of prefrontal N-methyl-d-aspartate (NMDA) receptor-mediated signaling in trace fear conditioning using the NMDA receptor antagonist 2-amino-5-phosphonovaleric acid (APV). Temporary inactivation of prefrontal activity with muscimol or blockade of NMDA receptor-dependent transmission in mPFC impaired the acquisition of trace, but not delay, conditional fear responses. Simultaneously acquired contextual fear responses were also impaired in drug-treated rats exposed to trace or delay, but not unpaired, training protocols. Our results support the idea that synaptic plasticity within the mPFC is critical for the long-term storage of memory in trace fear conditioning.The prefrontal cortex participates in a wide range of complex cognitive functions including working memory, attention, and behavioral inhibition (Fuster 2001). In recent years, the known functions of the prefrontal cortex have been extended to include a role in long-term memory encoding and retrieval (Blumenfeld and Ranganath 2006; Jung et al. 2008). The prefrontal cortex may be involved in the acquisition, expression, extinction, and systems consolidation of memory (Frankland et al. 2004; Santini et al. 2004; Takehara-Nishiuchi et al. 2005; Corcoran and Quirk 2007; Jung et al. 2008). Of these processes, the mechanisms supporting the acquisition of memory may be the least understood. Recently, the medial prefrontal cortex (mPFC) has been shown to be important for trace fear conditioning (Runyan et al. 2004; Gilmartin and McEchron 2005), which provides a powerful model system for studying the neurobiological basis of prefrontal contributions to memory. Trace fear conditioning is a variant of standard “delay” fear conditioning in which a neutral conditional stimulus (CS) is paired with an aversive unconditional stimulus (UCS). Trace conditioning differs from delay conditioning by the addition of a stimulus-free “trace” interval of several seconds separating the CS and UCS. Learning the CS–UCS association across this interval requires forebrain structures such as the hippocampus and mPFC. Importantly, the mPFC and hippocampus are only necessary for learning when a trace interval separates the stimuli (Solomon et al. 1986; Kronforst-Collins and Disterhoft 1998; McEchron et al. 1998; Takehara-Nishiuchi et al. 2005). This forebrain dependence has led to the hypothesis that neural activity in these structures is necessary to bridge the CS–UCS temporal gap. In support of this hypothesis, single neurons recorded from the prelimbic area of the rat mPFC exhibit sustained increases in firing during the CS and trace interval in trace fear conditioning (Baeg et al. 2001; Gilmartin and McEchron 2005). Similar sustained responses are not observed following the CS in delay conditioned animals or unpaired control animals. This pattern of activity is consistent with a working memory or “bridging” role for mPFC in trace fear conditioning, but it is not clear whether this activity is actually necessary for learning. We address this issue here using the γ-aminobutyric acid type A (GABA_A) receptor agonist muscimol to temporarily inactivate cellular activity in the prelimbic mPFC during the acquisition of trace fear conditioning.The contribution of mPFC to the long-term storage (i.e., 24 h or more) of trace fear conditioning, as opposed to a strictly working memory role (i.e., seconds to minutes), is a matter of some debate. Recent reports suggest that intact prefrontal activity at the time of testing is required for the recall of trace fear conditioning 2 d after training (Blum et al. 2006a), while mPFC lesions performed 1 d after training fail to disrupt the memory (Quinn et al. 2008). The findings from the former study may reflect a role for prelimbic mPFC in the expression of conditional fear rather than memory storage per se (Corcoran and Quirk 2007). However, blockade of the intracellular mitogen-activated protein kinase (MAPK) cascade during training impairs the subsequent retention of trace fear conditioning 48 h later (Runyan et al. 2004). Activation of the MAPK signaling cascade can result in the synthesis of proteins necessary for synaptic strengthening, providing a potential mechanism by which mPFC may participate in memory storage. To better understand the nature of the prefrontal contribution to long-term memory, more information is needed about fundamental plasticity mechanisms in this structure. Dependence on N-methyl-d-aspartate receptors (NMDAR) is a key feature of many forms of long-term memory, both in vitro and in vivo. The induction of long-term potentiation (LTP) in the hippocampus, a cellular model of long-term plasticity and information storage, requires NMDAR activation (Reymann et al. 1989). Genetic knockdown or pharmacological blockade of NMDAR-mediated neurotransmission in the hippocampus impairs several forms of hippocampus-dependent memory, including trace fear conditioning (Tonegawa et al. 1996; Huerta et al. 2000; Quinn et al. 2005), but it is unknown if activation of these receptors is necessary in the mPFC for the acquisition of trace fear conditioning. Data from in vivo electrophysiology studies have shown that stimulation of ventral hippocampal inputs to prelimbic neurons in mPFC produces LTP, and the induction of prefrontal LTP depends upon functional NMDARs (Laroche et al. 1990; Jay et al. 1995). If the role of mPFC in trace fear conditioning goes beyond simply maintaining CS information in working memory, then activation of NMDAR may be critical to memory formation. We test this hypothesis by reversibly blocking NMDAR neurotransmission with 2-amino-5-phosphonovaleric acid (APV) during training to examine the role of prefrontal NMDAR to the acquisition of trace fear conditioning.Another important question is whether mPFC contributes to the formation of contextual fear memories. Fear to the training context is acquired simultaneously with fear to the auditory CS in both trace and delay fear conditioning. Conflicting reports in the literature suggest the role of mPFC in contextual fear conditioning is unclear. Damage to ventral areas of mPFC prior to delay fear conditioning has failed to impair context fear acquisition (Morgan et al. 1993). Prefrontal lesions incorporating dorsal mPFC have in some cases been reported to augment fear responses to the context (Morgan and LeDoux 1995), while blockade of NMDAR transmission has impaired contextual fear conditioning (Zhao et al. 2005). Post-training lesions of mPFC impair context fear retention (Quinn et al. 2008) in trace and delay conditioning. Contextual fear responses were assessed in this study to determine the contribution of neuronal activity and NMDAR-mediated signaling in mPFC to the acquisition of contextual fear conditioning.     

The role of amygdala nuclei in the expression of auditory signaled two-way active avoidance in rats

Using a two-way signaled active avoidance (2-AA) learning procedure, where rats were trained in a shuttle box to avoid a footshock signaled by an auditory stimulus, we tested the contributions of the lateral (LA), basal (B), and central (CE) nuclei of the amygdala to the expression of instrumental active avoidance conditioned responses (CRs). Discrete or combined lesions of the LA and B, performed after the rats had reached an asymptotic level of avoidance performance, produced deficits in the CR, whereas CE lesions had minimal effect. Fiber-sparing excitotoxic lesions of the LA/B produced by infusions of N-methyl-d-aspartate (NMDA) also impaired avoidance performance, confirming that neurons in the LA/B are involved in mediating avoidance CRs. In a final series of experiments, bilateral electrolytic lesions of the CE were performed on a subgroup of animals that failed to acquire the avoidance CR after 3 d of training. CE lesions led to an immediate rescue of avoidance learning, suggesting that activity in CE was inhibiting the instrumental CR. Taken together, these results indicate that the LA and B are essential for the performance of a 2-AA response. The CE is not required, and may in fact constrain the instrumental avoidance response by mediating the generation of competing Pavlovian responses, such as freezing.Early studies of the neural basis of fear often employed avoidance conditioning procedures where fear was assessed by measuring instrumental responses that reduced exposure to aversive stimuli (e.g., Weiskrantz 1956; Goddard 1964; Sarter and Markowitsch 1985; Gabriel and Sparenborg 1986). Despite much research, studies of avoidance failed to yield a coherent view of the brain mechanisms of fear. In some studies, a region such as the amygdala would be found to be essential and in other studies would not. In contrast, rapid progress in understanding the neural basis of fear and fear learning was made when researchers turned to the use of Pavlovian fear conditioning (Kapp et al. 1984, 1992; LeDoux et al. 1984; Davis 1992; LeDoux 1992; Cain and Ledoux 2008a). It is now well established from such studies that specific nuclei and subnuclei of the amygdala are essential for the acquisition and storage of Pavlovian associative memories about threatening situations (LeDoux 2000; Fanselow and Gale 2003; Maren 2003; Maren and Quirk 2004; Schafe et al. 2005; Davis 2006).Several factors probably contributed to the fact that Pavlovian conditioning succeeded where avoidance conditioning struggled. First, avoidance conditioning has long been viewed as a two-stage learning process (Mowrer and Lamoreaux 1946; Miller 1948b; McAllister and McAllister 1971; Levis 1989; Cain and LeDoux 2008b). In avoidance learning, the subject initially undergoes Pavlovian conditioning and forms an association between the shock and cues in the apparatus. The shock is an unconditioned stimulus (US) and the cues are conditioned stimuli (CS). Subsequently, the subject learns the instrumental response to avoid the shock. Further, the “fear” aroused by the presence of the CS motivates learning of the instrumental response. Fear reduction associated with successful avoidance has even been proposed to be the event that reinforces avoidance learning (e.g., Miller 1948b; McAllister and McAllister 1971; Cain and LeDoux 2007). Given that Pavlovian conditioning is the initial stage of avoidance conditioning, as well as the source of the “fear” in this paradigm, it would be more constructive to study the brain mechanisms of fear through studies of Pavlovian conditioning rather than through paradigms where Pavlovian and instrumental conditioning are intermixed. Second, avoidance conditioning was studied in a variety of ways, but it was not as well appreciated at the time as it is today; that subtle differences in the way tasks are structured can have dramatic effects on the brain mechanisms required to perform the task. There was also less of an appreciation for the detailed organization of circuits in areas such as the amygdala. Thus, some avoidance studies examined the effects of removal of the entire amygdala or multiple subdivisions (for review, see Sarter and Markowitsch 1985). Finally, fear conditioning studies typically involved a discrete CS, usually a tone, which could be tracked from sensory processing areas of the auditory system to specific amygdala nuclei that process the CS, form the CS–US association, and control the expression of defense responses mediated by specific motor outputs. In contrast, studies of avoidance conditioning often involved diffuse cues, and the instrumental responses used to indirectly measure fear were complex and not easily mapped onto neural circuits.Despite the lack of progress in understanding the neural basis of avoidance responses, this behavioral paradigm has clinical relevance. For example, avoidance behaviors provide an effective means of dealing with fear in anticipation of a harmful event. When information is successfully used to avoid harm, not only is the harmful event prevented, but also the fear arousal, anxiety, and stress associated with such events; (Solomon and Wynne 1954; Kamin et al. 1963). Because avoidance is such a successful strategy to cope with danger, it is used extensively by patients with fear-related disorders to reduce their exposure to fear- or anxiety-provoking situations. Pathological avoidance is, in fact, a hallmark of anxiety disorders: In avoiding fear and anxiety, patients often fail to perform normal daily activities (Mineka and Zinbarg 2006).We are revisiting the circuits of avoidance conditioning from the perspective of having detailed knowledge of the circuit of the first stage of avoidance, Pavlovian conditioning. To most effectively take advantage of Pavlovian conditioning findings, we have designed an avoidance task that uses a tone and a shock. Rats were trained to shuttle back and forth in a runway in order to avoid shock under the direction of a tone. That is, the subjects could avoid a shock if they performed a shuttle response when the tone was on, but received a shock if they stayed in the same place (two-way signaled active avoidance, 2-AA). While the amygdala has been implicated in 2-AA (for review, see Sarter and Markowitsch 1985), the exact amygdala nuclei and their interrelation in a circuit are poorly understood.We focused on the role of amygdala areas that have been studied extensively in fear conditioning: the lateral (LA), basal (B), and central (CE) nuclei. The LA is widely thought to be the locus of plasticity and storage of the CS–US association, and is an essential part of the fear conditioning circuitry. The basal amygdala, which receives inputs from the LA (Pitkänen 2000), is not normally required for the acquisition and expression of fear conditioning (Amorapanth et al. 2000; Nader et al. 2001), although it may contribute under some circumstances (Goosens and Maren 2001; Anglada-Figueroa and Quirk 2005). The B is also required for the use of the CS in the motivation and reinforcement of responses in other aversive instrumental tasks (Killcross et al. 1997; Amorapanth et al. 2000). The CE, through connections to hypothalamic and brainstem areas (Pitkänen 2000), is required for the expression of Pavlovian fear responses (Kapp et al. 1979, 1992; LeDoux et al. 1988; Hitchcock and Davis 1991) but not for the motivation or reinforcement of aversive instrumental responses (Amorapanth et al. 2000; LeDoux et al. 2009). We thus hypothesized that damage to the LA or B, but not to the CE, would interfere with the performance of signaled active avoidance.     

Post-training reversible inactivation of the hippocampus enhances novel object recognition memory

Research on the role of the hippocampus in object recognition memory has produced conflicting results. Previous studies have used permanent hippocampal lesions to assess the requirement for the hippocampus in the object recognition task. However, permanent hippocampal lesions may impact performance through effects on processes besides memory consolidation including acquisition, retrieval, and performance. To overcome this limitation, we used an intrahippocampal injection of the GABA agonist muscimol to reversibly inactivate the hippocampus immediately after training mice in two versions of an object recognition task. We found that the inactivation of the dorsal hippocampus after training impairs object-place recognition memory but enhances novel object recognition (NOR) memory. However, inactivation of the dorsal hippocampus after repeated exposure to the training context did not affect object recognition memory. Our findings suggest that object recognition memory formation does not require the hippocampus and, moreover, that activity in the hippocampus can interfere with the consolidation of object recognition memory when object information encoding occurs in an unfamiliar environment.The medial temporal lobe plays an important role in recognition memory formation, as damage to this brain structure in humans, monkeys, and rodents impairs performance in recognition memory tasks (for review, see Squire et al. 2007). Within the medial temporal lobe, studies have consistently demonstrated that the perirhinal cortex is involved in this form of memory (Brown and Aggleton 2001; Winters and Bussey 2005; Winters et al. 2007, 2008; Balderas et al. 2008). In contrast, the role of the hippocampus in object recognition memory remains a source of debate. Some studies have reported novel object recognition (NOR) impairments in animals with hippocampal lesions (Clark et al. 2000; Broadbent et al. 2004, 2010), yet others have reported no impairments (Winters et al. 2004; Good et al. 2007). Differences in hippocampal lesion size and behavioral procedures among the different studies have been implicated as the source of discrepancy in these findings (Ainge et al. 2006), but previous studies have not examined the consequences of environment familiarity on the hippocampus dependence of object recognition memory.Previous studies addressing the role of the hippocampus in recognition memory relied on permanent, pre-training lesions (Clark et al. 2000; Broadbent et al. 2004; Winters et al. 2004; Good et al. 2007). Permanent lesions inactivate the hippocampus not only during the consolidation phase, but also during habituation, acquisition, and memory retrieval, potentially confounding interpretation of the results. Furthermore, permanent lesion studies require long surgery recovery times during which extrahippocampal changes may emerge to mask or compensate for the loss of hippocampal function. To overcome these problems, we reversibly inactivated the dorsal hippocampus after training mice in two versions of the object recognition task. We infused muscimol, a γ-aminobutyric acid (GABA) receptor type A agonist, into the dorsal hippocampus immediately after training in an object-place recognition task or immediately following training in a NOR task. Consistent with previous studies (Save et al. 1992; Galani et al. 1998; Mumby et al. 2002; Stupien et al. 2003; Aggleton and Brown 2005), we observed that hippocampal inactivation impairs object-place recognition memory. Interestingly, we observed that the degree of contextual familiarity can influence NOR memory formation. We found that when shorter periods of habituation to the experimental environment were used, hippocampal inactivation enhances long-term NOR memory. In contrast, after extended periods of contextual habituation, long-term recognition memory was unaltered by hippocampal inactivation. Together these results suggest that if familiarization with objects occurs at a stage in which the contextual environment is relatively novel, the hippocampus plays an inhibitory role on the consolidation of object recognition memory. Supporting this view, we observed that object recognition memory is unaffected by hippocampal inactivation when initial exploration of the objects occurred in a familiar environment.     

A molecular dissociation between cued and contextual appetitive learning

In appetitive Pavlovian learning, animals learn to associate discrete cues or environmental contexts with rewarding outcomes, and these cues and/or contexts can potentiate an ongoing instrumental response for reward. Although anatomical substrates underlying cued and contextual learning have been proposed, it remains unknown whether specific molecular signaling pathways within the striatum underlie one form of learning or the other. Here, we show that while the striatum-enriched isoform of adenylyl cyclase (AC5) is required for cued appetitive Pavlovian learning, it is not required for contextual appetitive learning. Mice lacking AC5 (AC5KO) could not learn an appetitive Pavlovian learning task in which a discrete signal light predicted reward delivery, yet they could form associations between context and either natural or drug reward, which could in turn elicit Pavlovian approach behavior. However, unlike wild-type (WT) mice, AC5KO mice could not use these Pavlovian conditioned stimuli to potentiate ongoing instrumental behavior in a Pavlovian-to-instrumental transfer paradigm. These data suggest that AC5 is specifically required for learning associations between discrete cues and outcomes in which the temporal relationship between conditioned stimulus (CS) and unconditioned stimulus (US) is essential, while alternative signaling mechanisms may underlie the formation of associations between context and reward. In addition, loss of AC5 compromises the ability of both contextual and discrete cues to modulate instrumental behavior.In Pavlovian learning, animals form associations between discrete or contextual stimuli in their environment to shape their behavior and make appropriate responses. In discrete cue appetitive Pavlovian conditioning, a single cue with a defined onset and offset that typically activates one sensory modality is provided, immediately followed by reward delivery (Hall 2002; Domjan 2006; Ito et al. 2006). Alternatively, behavior can be driven by context, an assortment of stimuli activating a number of sensory modalities that contribute to the representation of environmental space (Balsam 1985; Rudy and Sutherland 1995; Smith and Mizumori 2006). Collectively, these stimuli make up a context that is paired with reward delivery in contextual appetitive learning. One important distinction between these two forms of learning is that in cued conditioning, there is a discrete temporal relationship between conditioned stimulus (CS) and unconditioned stimulus (US). Thus, an animal can effectively anticipate timing of reward delivery from onset and offset of CS. In vivo studies of dopamine (DA) neuron activity have suggested this discrete temporal relationship can be encoded by DA neurons (Schultz et al. 1997; Schultz 1998a). In contrast, in many contextual Pavlovian conditioning tasks, US delivery is not predicted, it is delivered as the animal explores the environment; thus, the temporal relationship between contextual stimuli and reinforcement is not an essential component of the learned associations (Fanselow 2000). These two types of environmental stimuli may be encoded differently and mediated by different neural substrates.Lesion studies have elucidated the anatomical dissociations between cued and contextual appetitive learning. Using a modified Y-maze procedure, it has been suggested that contextual appetitive learning is hippocampus- and nucleus-accumbens (NAc) dependent, while cued learning is dependent on the basolateral nucleus of the amygdala (BLA) and the NAc (Ito et al. 2005, 2006). In addition, as the NAc processes glutamatergic inputs from the amygdala and the hippocampus (Groenewegen et al. 1999; Goto and Grace 2008), recent studies have indicated that disconnecting the hippocampus from the NAc shell can disrupt contextual appetitive conditioning (Ito et al. 2008). In addition to glutamatergic inputs, the NAc, as part of the ventral striatum, receives dense dopaminergic input from midbrain nuclei (Groenewegen et al. 1999). Temporal shifts in phasic DA release in striatal regions has been correlated with appetitive Pavlovian learning (Day et al. 2007), and models of striatal function suggest that DA-dependent modification of glutamatergic transmission in the striatum may underlie reinforcement learning (Reynolds et al. 2001; Reynolds and Wickens 2002).The cAMP pathway has been implicated in plasticity and learning in a number of neuronal structures (Abel et al. 1997; Ferguson and Storm 2004; Pittenger et al. 2006). Adenylyl cyclase (AC), the enzyme that makes cAMP, has nine membrane-bound isoforms, each with different expression patterns and regulatory properties (Hanoune and Defer 2001). AC5 is highly enriched in the striatum, with very low levels of expression in other regions of the brain (Mons et al. 1998; Iwamoto et al. 2003; Kheirbek et al. 2008, 2009), and genetic deletion of AC5 (AC5KO) severely compromises DA''s ability to modulate cAMP levels in the striatum (Iwamoto et al. 2003). Previous studies have shown that AC5KO mice were severely impaired in acquisition of a cued appetitive Pavlovian learning task, while formation of action–outcome contingencies in instrumental learning was intact (Kheirbek et al. 2008). Yet, it remains unknown whether the cAMP pathway in the striatum underlies all forms of appetitive Pavlovian learning, or how it contributes to the ability of Pavlovian cues to modulate instrumental behavior.In this study, we asked if genetic deletion of AC5 selectively impairs cued or contextual appetitive learning. In addition, we tested whether loss of AC5 affects the ability of conditioned cues or contexts to modulate instrumental behavior. Our data indicate that although loss of AC5 abolishes cued appetitive learning, contextual learning is spared. Although contextual stimuli could elicit approach behavior in AC5KO mice, they could not potentiate an ongoing instrumental response, highlighting the importance of this isoform of AC in Pavlovian–instrumental interactions.     

Individual differences in spatial pattern separation performance associated with healthy aging in humans

Rodent studies have suggested that “pattern separation,” the ability to distinguish among similar experiences, is diminished in a subset of aged rats. We extended these findings to the human using a task designed to assess spatial pattern separation behavior (determining at time of test whether pairs of pictures shown during the study were in the same spatial locations). Using a standardized test of word recall to divide healthy aged adults into impaired and unimpaired groups relative to young performance, we demonstrate that aged impaired adults are biased away from pattern separation and toward pattern completion, consistent with the rodent studies.Memory impairment is a common complaint among aging individuals, yet the variability within the aging population is great in both rats (Gallagher et al. 2006; Robitsek et al. 2008) and humans (Hilborn et al. 2009). A rodent model of aging (Gallagher et al. 2006; Wilson et al. 2006) has demonstrated that ∼50% of healthy rats qualify as cognitively “impaired” by scoring outside the range of the young performance in a standard protocol (Gallagher et al. 1993). The other half, the “unimpaired” rats, perform on par with young adults, demonstrating a natural degree of variability in cognitive aging. In this study, we sought to capitalize on the variability observed in the aging of both rats and humans in a study of spatial pattern separation.One source of variability in memory performance is hypothesized to be tied to changes in the input to the dentate gyrus (DG), which has been shown in the rat to be affected by the aging process. Smith et al. (2000) reported a selective impairment in layer II entorhinal input into the DG and CA3 regions of the hippocampus in rats with cognitive impairment. Similarly, the number of synapses in the outer receiving layer of DG was reduced in autopsied aged brains and correlated with earlier performance on a delayed recall task (Scheff et al. 2006). Finally, in a human imaging study, Small et al. (2002) observed that 60% of their aging sample demonstrated diminished MRI signal in the hippocampal region (including the DG) and also had a greater decline in memory performance. These findings support the notion that changes in the DG associated with aging may affect memory performance.The DG may be particularly important for the computations that underlie pattern separation (Treves and Rolls 1994; McClelland et al. 1995; Norman and O''Reilly 2003). “Pattern separation” refers to the process by which similar inputs are stored as distinct, nonoverlapping representations. In contrast, “pattern completion” refers to the process by which an existing representation can be reinstated by the presentation of a partial or degraded cue. Numerous studies in the rodent have identified the importance of the DG for pattern separation using electrophysiological methods (Leutgeb et al. 2004, 2005, 2007; Leutgeb and Leutgeb 2007), immediate early gene expression (Vazdarjanova and Guzowski 2004), lesions (Lee et al. 2005; Gilbert and Kesner 2006; Goodrich-Hunsaker et al. 2008), and even genetic manipulations (Cravens et al. 2006; Kubik et al. 2007; McHugh et al. 2008). Human neuroimaging has also recently identified activity in the DG (and CA3 regions of the hippocampus) in an object pattern separation task (Kirwan and Stark 2007; Bakker et al. 2008).Given the importance of the DG in pattern separation and its vulnerability to changes that occur with aging, studies have begun to examine pattern separation in older adults. Our laboratory has designed a task to examine object-based pattern separation performance in humans (Kirwan and Stark 2007). In this task, pictures of objects were presented either once or repeatedly throughout the task. Critically, some of the items presented were lures that were similar but not identical to previously shown items. The overlapping features of the lures more heavily engaged pattern separation processes. In young adults, functional magnetic resonance imaging (fMRI) activity in the DG was sensitive to the lures, indicating a role in pattern separation processes in both an explicit (Kirwan and Stark 2007) and implicit (Bakker et al. 2008) version of this task. Toner et al. (2009) used the explicit version of this task to demonstrate that older adults showed a greater tendency to identify lures as “old” (repeated) relative to young adults. These findings were also recently replicated in our laboratory (Yassa et al., in press), with the additional demonstration that older adults exhibit greater fMRI CA3/DG activity for the lures during both encoding and retrieval.Since object-based pattern separation appears to be modulated by the DG in humans, we wondered if these findings could be extended to spatial pattern separation. Rodent studies have demonstrated that the DG has a particular role in spatial pattern separation (Gilbert et al. 2001; Kesner et al. 2004). Specifically, Hunsaker et al. (2008) placed rats with localized DG lesions in an environment with two objects spaced 60 cm apart. When the animals were later placed in the same environment with the same objects now placed 40 cm apart, DG-lesioned animals (unlike control animals) did not re-explore the objects or environment. These data suggest that the DG-lesioned rats were not able to discriminate between the training and test environments. That is, they were impaired in spatial pattern separation. Since converging evidence suggests that one feature of the aging process can be characterized as a DG knockdown, we modified this task design for humans to test spatial pattern separation performance in older adults. While the Hunsaker et al. (2008) task emphasized the distance between the two objects as the source of interference creating a greater need for pattern separation, the paradigm presented here moves an object in any direction, changing both the distance and the angle (i.e., changing more of the spatial relations). We posit that this amount of movement (close, medium, or far) may place similar demands on spatial pattern separation processes as in the rodent task.The present study included 20 young adults (mean age 19.9 yr, range 18–27 yr) and 30 aged adults (mean age 70.4 yr, range 59–80 yr). Aged adults completed a battery of standardized neuropsychological tests, including the Mini-Mental State Exam (Folstein et al. 1975), Rey Auditory–Verbal Learning Task (RAVLT) (Rey 1941), Digit Span, Vocabulary, and Matrices subtests from the Wechsler Adult Intelligence Scale III (Wechsler 1997). The Vocabulary and Matrices scores were entered into a weighted formula along with age, gender, and education to derive estimated IQ scores (Schoenberg et al. 2003). All aged participants scored within the normal age-adjusted ranges on these measures and were cognitively intact. Younger adults also completed the RAVLT and scored within the normal age-adjusted range. These data are presented in Table 

Entorhinal cortical Island cells regulate temporal association learning with long trace period

Temporal association learning (TAL) allows for the linkage of distinct, nonsynchronous events across a period of time. This function is driven by neural interactions in the entorhinal cortical–hippocampal network, especially the neural input from the pyramidal cells in layer III of medial entorhinal cortex (MECIII) to hippocampal CA1 is crucial for TAL. Successful TAL depends on the strength of event stimuli and the duration of the temporal gap between events. Whereas it has been demonstrated that the neural input from pyramidal cells in layer II of MEC, referred to as Island cells, to inhibitory neurons in dorsal hippocampal CA1 controls TAL when the strength of event stimuli is weak, it remains unknown whether Island cells regulate TAL with long trace periods as well. To understand the role of Island cells in regulating the duration of the learnable trace period in TAL, we used Pavlovian trace fear conditioning (TFC) with a 60-sec long trace period (long trace fear conditioning [L-TFC]) coupled with optogenetic and chemogenetic neural activity manipulations as well as cell type-specific neural ablation. We found that ablation of Island cells in MECII partially increases L-TFC performance. Chemogenetic manipulation of Island cells causes differential effectiveness in Island cell activity and leads to a circuit imbalance that disrupts L-TFC. However, optogenetic terminal inhibition of Island cell input to dorsal hippocampal CA1 during the temporal association period allows for long trace intervals to be learned in TFC. These results demonstrate that Island cells have a critical role in regulating the duration of time bridgeable between associated events in TAL.

The linkage of temporally discontiguous events, called temporal association learning (TAL), is an essential function for episodic memory formation; for animals, when an event took place, and in what order a series of events occurred is directly linked to adaptation to continuous changes in the environment (Eichenbaum 2000; Tulving 2002a,b; Kitamura et al. 2015a; Kitamura 2017; Pilkiw and Takehara-Nishiuchi 2018). The entorhinal cortical–hippocampal (EC-HPC) network in particular is currently considered to bridge the temporal discontinuity between events (Solomon et al. 1986; Moyer et al. 1990; Wallenstein et al. 1998; McEchron et al. 1999; Eichenbaum 2000; Huerta et al. 2000; Ryou et al. 2001; Takehara et al. 2003; Chowdhury et al. 2005; Esclassan et al. 2009; Morrissey et al. 2012; Suter et al. 2013; Sellami et al. 2017; Wilmot et al. 2019).Two major excitatory inputs to HPC arise from the superficial layers of the EC (Fig. 1A), forming the direct (monosynaptic), and indirect (trisynaptic) pathways (Amaral and Witter 1989; Amaral and Lavenex 2007; Kitamura 2017; Kitamura et al. 2017). While pyramidal cells in EC layer III (ECIII cells) project directly to CA1 (Kohara et al. 2014; Kitamura et al. 2015b), the trisynaptic pathway originates from excitatory Reelin⁺ stellate cells in EC layer II (ECII) projecting directly to DG, CA3, and CA2 (Fig. 1B; Tamamaki and Nojyo 1993; Varga et al. 2010). CalbindinD-28K⁺/Wolfram syndrome 1 (Wfs1)⁺ pyramidal cells, another excitatory neural population in EC layer II called “Island cells,” form cell clusters along the ECII/ECI border (Alonso and Klink 1993; Fujimaru and Kosaka 1996; Klink and Alonso 1997; Kawano et al. 2009; Varga et al. 2010; Kitamura et al. 2014; Ray et al. 2014) and directly project to the GABAergic interneurons of stratum lacunosum (SL-INs) in HPC CA1 and drive feedforward inhibition to HPC CA1 pyramidal cells (Fig. 1B; Kitamura et al. 2014; Surmeli et al. 2016; Kitamura 2017; Ohara et al. 2018; Yang et al. 2018; Zutshi et al. 2018).Open in a separate windowFigure 1.Circuit schematic diagram of the medial entorhinal cortex (MEC)–hippocampal (HPC) circuit. (A) Major projections in the entorhinal cortical (EC)-HPC network. ECIII neurons (green) project directly to CA1. ECII Ocean cells (ECIIo, purple) project to the dentate gyrus (DG) (light blue)/CA3 (pink) initiating the trisynaptic pathway. ECII Island cells (ECIIi, blue) project directly into CA1. (B) ECIII projections (green) excite the distal portions of CA1 pyramidal cell (yellow) dendrites in the stratum moleculare. Island cells (ECIIi, blue) excite the interneurons of stratum lacunosum (SL-INs, red), which in turn inhibit the distal dendrites of CA1 pyramidal cells in SL.Trace fear conditioning (TFC) has been established as one suitable animal model for TAL (Fendt and Fanselow 1999; Maren 2001; Kim and Jung 2006) that can be also used as a translational bridge between animal and human learning (Clark and Squire 1998; Buchel and Dolan 2000; Delgado et al. 2006). Lesion, pharmacological, molecular, and optogenetic manipulation, as well as disease models in medial entorhinal cortex (MEC), demonstrate that MEC is crucial for TFC and temporal learning (Ryou et al. 2001; Woodruff-Pak 2001; Runyan et al. 2004; Esclassan et al. 2009; Gilmartin and Helmstetter 2010; Suh et al. 2011; Morrissey et al. 2012; Shu et al. 2016; Hales et al. 2018; Yang et al. 2018; Heys et al. 2020). Specifically, MECIII inputs into the HPC CA1 pyramidal cells are essential for the formation of TFC (Yoshida et al. 2008; Suh et al. 2011; Kitamura et al. 2014; Kitamura 2017). However, the temporal association function driven by MECIII neurons must be regulated for optimal adaptive memory formation, as too strong an association of a particular pair of events may interfere with associations of other useful pairs, whereas too weak an association for a given pair of events, in terms of weaker impact of events or longer duration of temporal gap between events, would not result in an effective memory (Kitamura et al. 2015a; Marks et al. 2020). In a naturalistic context, this would mean that more distant/quieter sounds, less intense somatic sensations (e.g., pain), or increased temporal distance between any two events would signal that the events are less likely to be causally associated, therefore less relevant, and less likely to be stored and recalled. In fact, successful TFC depends on the strength of event stimuli and duration of temporal gap between events (Stiedl and Spiess 1997; Misane et al. 2005; Kitamura et al. 2014; Kitamura 2017). However, the underlying regulatory mechanism for TAL remains hidden. Previously we demonstrated that feedforward inhibition by Island cells acts as a gating controller for the MECIII inputs to the distal dendrites of HPC CA1 pyramidal cells in stratum moleculare (SM) (Kitamura et al. 2014) to control TFC when weaker (in this case diminished footshock intensity) unconditioned stimuli were delivered for TFC, indicating that Island cell activity controls the temporal association when the strength of two discontinuous events are relatively weaker. However, the way in which the EC-HPC network regulates TFC with a longer trace period still remains unknown. Because the activation of Island cells would result in a net inhibitory effect on the local network in CA1, imposing a tight and specific regulation on associations of events across the temporal gap in TAL (Crestani et al. 2002; Moore et al. 2010; Kitamura et al. 2014, 2015b), we hypothesized that the length of the temporal gap between events would also be modulated by this mechanism. In this study, we examined the role of the regulatory input to this circuit arising specifically from the Island cells in the MECII using apoptotic elimination of Island cells, chemogenetic neural inhibition, and optogenetic terminal inhibition methods within an L-TFC protocol to give a thorough and complete assessment of the circuit involvement while considering each technique''s unique features.     

Time-dependent transformations of memory representations differ along the long axis of the hippocampus

Research has shown that sleep is beneficial for the long-term retention of memories. According to theories of memory consolidation, memories are gradually reorganized, becoming supported by widespread, distributed cortical networks, particularly during postencoding periods of sleep. However, the effects of sleep on the organization of memories in the hippocampus itself remains less clear. In a 3-d study, participants encoded separate lists of word–image pairs differing in their opportunity for sleep-dependent consolidation. Pairs were initially studied either before or after an overnight sleep period, and were then restudied in a functional magnetic resonance imaging (fMRI) scan session. We used multivariate pattern similarity analyses to examine fine-grained effects of consolidation on memory representations in the hippocampus. We provide evidence for a dissociation along the long axis of the hippocampus that emerges with consolidation, such that representational patterns for object–word memories initially formed prior to sleep become differentiated in anterior hippocampus and more similar, or overlapping, in posterior hippocampus. Differentiation in anterior hippocampal representations correlated with subsequent behavioral performance. Furthermore, representational overlap in posterior hippocampus correlated with the duration of intervening slow wave sleep. Together, these results demonstrate that sleep-dependent consolidation promotes the reorganization of memory traces along the long axis of the hippocampus.

The hippocampus has long been considered critical for encoding new memories; however, the effects of consolidation on hippocampal memory traces has remained an area of active research. Memories are thought to be stabilized for the long term as they become distributed across neocortical networks (Buzsáki 1989; Alvarez and Squire 1994; McClelland et al. 1995), a process supported by mechanisms during sleep (Diekelmann and Born 2010; Rasch and Born 2013). Whereas much research has been devoted to understanding the hippocampal contributions to the long-term retention of memories, open questions remain in considering how sleep-dependent consolidation affects the organization of hippocampal traces.The hippocampus has previously been shown to be critical for binding disparate elements of an experience together (Cohen and Eichenbaum 1993; Davachi 2006). Theories suggest that the hippocampus quickly encodes new experiences, while the cortex, with a slower learning rate, gradually comes to represent the central features from this hippocampal trace, resulting in abstracted memories that can be integrated into long-term cortical stores (McClelland et al. 1995). Prior research has demonstrated evidence for a coordinated hippocampal–cortical dialogue during sleep (Andrade et al. 2011; Bergmann et al. 2012; Ngo et al. 2020) as well as enhanced hippocampal–cortical functional connectivity after learning, facilitating the retention of memories (Tambini et al. 2010; Tompary et al. 2015; Murty et al. 2017; Cowan et al. 2021). Reports suggest consolidation results in more integrated cortical memory traces in the cortex (Richards et al. 2014; Tompary and Davachi 2017; Cowan et al. 2020); however, it remains an open question whether the active consolidation processes that support memory reorganization across hippocampal–cortical networks also transform hippocampal memory traces.Research on the fate of the hippocampal trace with consolidation has often focused on questions about the permanence of memories in the hippocampus. Theories of systems consolidation have classically debated whether the hippocampal trace is time-limited (Alvarez and Squire 1994), or, rather, whether the hippocampus continues to represent memories in perpetuity (Nadel and Moscovitch 1997; Winocur and Moscovitch 2011; Moscovitch et al. 2016; Sekeres et al. 2018a). Another theory posits that while the original hippocampal trace is transient, during retrieval the hippocampus reconstructs details of an experience from cortical traces (Barry and Maguire 2019). Much research in this vein has focused on investigating changes in hippocampal blood-oxygenation level-dependent (BOLD) univariate activation with time (Bosshardt et al. 2005a,b; Takashima et al. 2006, 2009; Gais et al. 2007; Sterpenich et al. 2007, 2009; Yamashita et al. 2009; Milton et al. 2011; Watanabe et al. 2012; Ritchey et al. 2015; Baran et al. 2016; Dandolo and Schwabe 2018) and the effects of hippocampal lesions in animals and humans (Winocur et al. 2001; Frankland and Bontempi 2005; Winocur and Moscovitch 2011; Moscovitch et al. 2016) with mixed results. Interestingly, pinpointing these effects along the long axis of the hippocampus has also proven unclear. Some reports have found that only the anterior hippocampus exhibits time-dependent changes in retrieval-related univariate activation, with evidence of decreases with delay (Takashima et al. 2006; Milton et al. 2011; Dandolo and Schwabe 2018), but also evidence of greater activation for more remote, compared with recent, memories (Bosshardt et al. 2005a,b). At the same time, other studies have found decreases in univariate activation only in the posterior hippocampus (Bosshardt et al. 2005b; Takashima et al. 2009; Yamashita et al. 2009; Milton et al. 2011; Watanabe et al. 2012; Ritchey et al. 2015; Sekeres et al. 2018b).Because of these conflicting findings, instead of asking just about dependence or overall changes in activation in the hippocampus, theories and empirical research have instead increasingly considered the organization of memory representations in the hippocampus (Robin and Moscovitch 2017; Sekeres et al. 2018a). Broadly, using representational similarity analyses, several studies have shown that hippocampal memory representations tend to become differentiated over learning, particularly for memories with overlapping content (LaRocque et al. 2013; Schlichting et al. 2015; Chanales et al. 2017; Brunec et al. 2020). Furthermore, it has been suggested that information is represented at different scales or “granularity” along the long axis of the hippocampus, in line with place field size differences (Kjelstrup et al. 2008; Komorowski et al. 2013), with anterior hippocampus representing more similar, coarse-grained, or gist-like information, while the posterior hippocampus represents fine-grained, detail-oriented representations (Evensmoen et al. 2013; Poppenk et al. 2013; Robin and Moscovitch 2017; Brunec et al. 2018, 2020). However, limited work has investigated whether this representational organization is altered with consolidation. Reports have shown that memory representations sharing overlapping content become more similar over a delay (Tompary and Davachi 2017; Audrain and McAndrews 2020), yet other work has found that hippocampal representations were not modulated by time (Ritchey et al. 2015; Ezzyat et al. 2018). Intriguingly, reports indicating greater differentiation in memories in anterior compared with posterior hippocampus with consolidation (Tompary and Davachi 2017; Dandolo and Schwabe 2018; Ezzyat et al. 2018) raise the possibility that the representational granularity along the anteroposterior axis may be transformed with consolidation. Thus, more work is needed to understand how consolidation influences the representational structure of memories in the hippocampus. In particular, despite much research connecting sleep to consolidation (Diekelmann and Born 2010; Rasch and Born 2013), it remains unknown whether sleep-dependent processes facilitate such delay-dependent transformations to the hippocampus.Active processes in the sleeping brain seem to be optimized for systems consolidation. Currently, the best mechanistic evidence for sleep-dependent consolidation comes from studies on hippocampal replay showing the repeated reactivation of encoding-related patterns of hippocampal activity (Buzsáki 1989; Wilson and McNaughton 1994; Girardeau and Zugaro 2011), which seems to be coordinated with replay in areas of the cortex (Ji and Wilson 2007; Peyrache et al. 2009; Wierzynski et al. 2009). It is thought that the coupling between oscillations during non-REM sleep stages (particularly slow wave sleep [SWS])—including sharp wave ripples that support replay, thalamocortical spindles, and slow oscillations—facilitates the hippocampal–cortical dialogue and information transfer to the cortex (Buzsáki 1996; Sirota et al. 2003; Steriade 2006; Clemens et al. 2011; Mölle and Born 2011; Staresina et al. 2015). Indeed, our previously published work from the present study provided supporting evidence that the density of thalamocortical sleep spindles (11–16 Hz) during overnight sleep is related to enhanced hippocampal–cortical functional connectivity measures, and increased similarity, or greater representational overlap, among memories in the ventromedial prefrontal cortex (vmPFC) (Cowan et al. 2020). Yet, while some prior work has shown that features of sleep, including spindle density and the duration of non-REM SWS, are related to decreased retrieval-related hippocampal activation for memoranda learned prior to sleep (Takashima et al. 2006; Baran et al. 2016; Hennies et al. 2016), it remains unclear how the reactivation of hippocampal traces during replay may impact the way memories are organized along the long axis of the hippocampus.To examine the effects of sleep-dependent consolidation on the neural representation of memories in the hippocampus, we designed a within-participant 3-d study using overnight polysomnography (PSG), functional magnetic resonance imaging (fMRI), and behavioral measures of memory (Fig. 1). In this study, aspects of which have been previously published (Cowan et al. 2020), participants first studied a list of word–image pairs before sleeping overnight (Sleep List), during which PSG was recorded. Upon waking in the morning, participants studied a new list of pairs (Morning List). The word–image pairs from these two lists were then restudied while undergoing an fMRI scan, intermixed with a third, novel list of pairs (Single Study List). Associative memory was tested immediately after the scan and again 24 h later. We compared measures of multivariate pattern similarity and univariate BOLD signal for the lists learned prior to, or after, sleep to probe how modulating the opportunity for sleep-dependent consolidation impacts the way memories are organized across the long axis of the hippocampus. Furthermore, our design allowed us to examine how features of overnight sleep are related to the representational organization of memories learned prior to the sleep period, as well as the behavioral benefit of changes to the organization of these memories. Thus, our study provides a novel examination of the effects of sleep-dependent consolidation on the representation of memories along the long axis of the hippocampus.Open in a separate windowFigure 1.Study design. For all encoding and restudy sessions, participants were asked to form an association between a word and an image. Participants first encoded the Sleep List (blue) before sleeping overnight while polysomnography was recorded. The next morning (day 2), participants encoded a second set of novel word–image pairs (Morning List). After a short delay (∼2 h), participants restudied these two sets of pairs, intermixed with novel pairs (Single Study List) in the functional magnetic resonance imaging (fMRI) scanner. Source memory was tested immediately after the scan and after a 24-h delay (day 3).     

The basolateral amygdala and nucleus accumbens core mediate dissociable aspects of drug memory reconsolidation

A distributed limbic-corticostriatal circuitry is implicated in cue-induced drug craving and relapse. Exposure to drug-paired cues not only precipitates relapse, but also triggers the reactivation and reconsolidation of the cue-drug memory. However, the limbic cortical-striatal circuitry underlying drug memory reconsolidation is unclear. The aim of this study was to investigate the involvement of the nucleus accumbens core and the basolateral amygdala in the reconsolidation of a cocaine-conditioned stimulus-evoked memory. Antisense oligodeoxynucleotides (ASO) were infused into each structure to knock down the expression of the immediate-early gene zif268, which is known to be required for memory reconsolidation. Control infusions used missense oligodeoxynucleotides (MSO). The effects of zif268 knockdown were measured in two complementary paradigms widely used to assess the impact of drug-paired CSs upon drug seeking: the acquisition of a new instrumental response with conditioned reinforcement and conditioned place preference. The results show that both intranucleus accumbens core and intrabasolateral amygdala zif268 ASO infusions at memory reactivation impaired the reconsolidation of the memory underlying a cocaine-conditioned place preference. However, knockdown of zif268 in the nucleus accumbens at memory reactivation had no effect on the memory underlying the conditioned reinforcing properties of the cocaine-paired CS measured subsequently, and this is in contrast to the marked impairment observed previously following intrabasolateral amygdala zif268 ASO infusions. These results suggest that both the basolateral amygdala and nucleus accumbens core are key structures within limbic cortical-striatal circuitry where reconsolidation of a cue-drug memory occurs. However reconsolidation of memory representations formed during Pavlovian conditioning are differentially localized in each site.Through Pavlovian association with the effects of addictive drugs, a conditioned stimulus (CS) acquires both general motivational and sensory-specific conditioned reinforcing properties (Everitt et al. 2000). These associations contribute to the high likelihood of relapse in addicted individuals, yet the extinction of drug CSs by nonreinforced exposure has proved to be of limited therapeutic utility (Conklin and Tiffany 2002). In abstinent humans, drug CSs evoke salient and persistent memories of drug-taking experiences, inducing craving and relapse (Childress et al. 1988; O''Brien et al. 1992), while in animals they also precipitate relapse to, or reinstatement of, drug-seeking behavior (de Wit and Stewart 1981; Meil and See 1996; Fuchs et al. 1998; Weiss 2000). Thus, disrupting drug-related memories might significantly diminish relapse propensity on subsequent exposure to drug-paired CSs, and thereby promote abstinence.Exposure to a drug-associated CS also triggers a process of memory reconsolidation, which restabilizes the reactivated and labile memory (Nader 2003). While reconsolidation may adaptively update memories (Dudai 2006; Hupbach et al. 2007; Rossato et al. 2007; Lee 2009), its disruption may reduce the impact of intrusive or aberrant memories on behavior subsequently (Lee et al. 2005, 2006; Brunet et al. 2008; Kindt et al. 2009; Taubenfeld et al. 2009). The reconsolidation of CS–cocaine memories has been shown to depend upon protein synthesis and expression of the plasticity-associated immediate-early gene, zif268, in the basolateral amygdala (BLA), since zif268 knockdown at memory reactivation disrupted the acquired conditioned reinforcing properties of the CS measured in drug-seeking tasks days or weeks later (Lee et al. 2005, 2006).Although the BLA has an established role in CS-drug memory reconsolidation, it remains unclear whether other sites within limbic cortical-ventral striatal circuitry participate in this process. The nucleus accumbens core (AcbC) is a primary candidate, as zif268 is up-regulated in the AcbC as well as in the BLA following exposure to cocaine CSs (Thomas et al. 2003). Furthermore, the AcbC, which is strongly implicated in Pavlovian influences on drug seeking and relapse (Cardinal et al. 2002; Kalivas and McFarland 2003), has been shown to be a site where the reconsolidation of a drug conditioned place preference (CPP) memory can be disrupted (Miller and Marshall 2005).Given the evidence of increased zif268 expression in the AcbC following CS-drug memory reactivation, we investigated its requirement in the reconsolidation of cocaine-associated memories. To address this issue, we employed two different but complementary paradigms widely used to measure the conditioned effects of CSs associated with drugs of abuse: the acquisition of a new instrumental response with conditioned reinforcement (ANR) and CPP. These procedures have been used successfully to investigate the mechanisms underlying the reconsolidation of appetitive Pavlovian memories, but it is likely that they depend upon different associative mechanisms (Everitt et al. 1991; White and McDonald 1993) that in turn depend upon different neural loci within limbic cortical-striatal circuitry (Cardinal et al. 2002). Therefore, to enable a full comparison with the functional involvement of the BLA, we investigated the necessity for BLA zif268 expression in drug memory reconsolidation as assessed in the CPP paradigm.     

Memory deficits are associated with impaired ability to modulate neuronal excitability in middle-aged mice

Normal aging disrupts hippocampal neuroplasticity and learning and memory. Aging deficits were exposed in a subset (30%) of middle-aged mice that performed below criterion on a hippocampal-dependent contextual fear conditioning task. Basal neuronal excitability was comparable in middle-aged and young mice, but learning-related modulation of the post-burst afterhyperpolarization (AHP)—a general mechanism engaged during learning—was impaired in CA1 neurons from middle-aged weak learners. Thus, modulation of neuronal excitability is critical for retention of context fear in middle-aged mice. Disruption of AHP plasticity may contribute to contextual fear deficits in middle-aged mice—a model of age-associated cognitive decline (AACD).Plasticity of intrinsic neuronal excitability increases the overall storage capacity of neurons and therefore likely plays a critical role in learning and memory (Zhang and Linden 2003). Increased neuronal excitability via reductions of the post-burst afterhyperpolarization (AHP) is hypothesized as a general mechanism underlying learning and memory tasks (Disterhoft et al. 1986; Disterhoft and Oh 2006). The AHP serves to limit subsequent firing in response to excitation (Madison and Nicoll 1984; Lancaster and Adams 1986; Storm 1990; Sah and Bekkers 1996). Generally speaking, the size of the AHP is inversely related to neuronal excitability, and the measurement of the AHP is routinely used as an index of neuronal excitability.Our laboratory and others have shown that AHP reductions are observed in hippocampal neurons from animals that learn hippocampal-dependent tasks including trace eyeblink conditioning in rabbit and rat (de Jonge et al. 1990; Moyer Jr et al. 1996, 2000; Kuo 2004) and spatial water maze in rat and mouse (Oh et al. 2003; Tombaugh et al. 2005; Ohno et al. 2006b). Learning-related reductions in the AHP have also been observed in cortical neurons following odor discrimination (Saar et al. 1998) and extinction learning (Santini et al. 2008). In vitro, activity-dependent plasticity of the AHP is induced using physiologically relevant stimuli (Kaczorowski et al. 2007). Because the AHP serves to limit subsequent firing, learning-related reductions in the AHP are poised to facilitate mechanisms crucial for information storage, such as long-term potentiation (LTP), synaptic integration (Sah and Bekkers 1996), metaplasticity (Le Ray et al. 2004), and spike-timing dependent plasticity (STDP) (Le Ray et al. 2004).Hippocampal neurons from naïve aged rodents and rabbits show a decrement in basal excitability evidenced by a robust enhancement of the AHP (Landfield and Pitler 1984; Moyer Jr et al. 1992, 2000; Oh et al. 1999; Kumar and Foster 2002, 2004; Power et al. 2002; Hemond and Jaffe 2005; Murphy et al. 2006b; Gant and Thibault 2008). Enhancement of the AHP in hippocampal neurons in aged animals correlates with impaired performance on learning paradigms that depend on a functional hippocampus, such as trace eyeblink and spatial water maze (Moyer Jr et al. 2000; Tombaugh et al. 2005; Murphy et al. 2006a). Pharmaceuticals aimed at reducing the AHP and increasing basal excitability (Moyer Jr et al. 1992; Moyer Jr and Disterhoft 1994) have been successful at restoring performance of aged rats on trace eyeblink conditioning (Deyo et al. 1989; Straube et al. 1990; Kowalska and Disterhoft 1994). Interestingly, AHPs from neurons recorded from aged learners are indistinguishable from young learners; both are reduced compared to that of aged weak-learners (Moyer Jr et al. 2000; Tombaugh et al. 2005). These data suggest that mechanisms that permit learning-related modulation of the AHP are also critical determinants of learning abilities in an aged population. To date, age-related impairments in hippocampal-dependent tasks and biophysical alterations in hippocampal neurons have largely focused on studies that compare animals at extreme ends of the aging spectrum.In an effort to better understand physiological changes that underlie the onset of early cognitive decline, the development of rodent models of “normal” age-associated cognitive decline (AACD), as well as mild cognitive impairment (MCI), is critical (Pepeu 2004). Therefore, we set out to characterize the development of age-related deficits indicative of hippocampal dysfunction in middle-aged C57Bl6/SJL mice and to examine the biophysical changes in hippocampal neurons that accompany such deficits.Recently, age-related deficits in contextual fear memory following trace fear conditioning were reported in a subset of middle-aged rats (Moyer Jr and Brown 2006). Because the dorsal hippocampus is critical for trace and contextual fear conditioning in mice and rats (McEchron et al. 1998; Chowdhury et al. 2005; Misane et al. 2005), trace fear conditioning is an ideal paradigm for exploring cellular mechanisms that underlie early-age-related cognitive decline.Here we investigate the effects of “early” aging on trace fear conditioning by comparing performance outcomes of young (2 mo, n = 7; and 4 mo, n = 8) and middle-aged (8 mo, n = 22) male C57/SJL F1 hybrid mice. Mice were trained and tested singly, and the experimenter was blind to the training and retention status of the mice. All animal procedures were approved by the Northwestern University Animal Care and Use Committee. Preliminary data were reported previously (Kaczorowski 2006).To assess hippocampal function with aging, young and middle-aged mice were trained on a trace fear conditioning task followed by retention tests of the auditory conditioned stimulus (CS) and contextual CS memory. The basic protocol for trace fear conditioning has been described previously (Ohno et al. 2006a). Mice were trained in a Plexiglas conditioning chamber with a stainless-steel floor grid used for shock delivery. After the baseline period (150 sec), mice received four pairings of the CS (tone; 15 sec, 3 kHz, 75 dB) and US (shock; 1 sec, 0.7 mA). The CS and unconditioned stimulus (US) were separated by a 30-sec empty trace interval. The intertrial interval was set at 210 ± 10 sec. The training chamber was wiped with 95% ethyl alcohol, illuminated with a 10-W bulb in an otherwise dark room, and provided with 65-dB white noise to make it distinct. During training on trace fear conditioning, no effect of age was observed on measures of baseline freezing (F_(2,34) = 2.0, P = 0.15), the expression of freezing during tone (F_(2,34) = 0.6, P = 0.6), or post-shock freezing (F_(2,34) = 0.2, P = 0.8), suggesting that middle-aged and young mice do not differ in measures of anxiolysis or expression of behavioral freezing (measured index of fear) (Fig. 1A).Open in a separate windowFigure 1.Onset of early aging deficits in 8-mo-old middle-aged mice. (A) Baseline (BL) freezing and auditory CS freezing during trace fear conditioning was similar between young (2 mo and 4 mo) and middle-aged (8 mo) mice. (B1) Mean baseline freezing and retention of the auditory CS memory (tones 1–4) were comparable in young (2 mo and 4 mo) and middle-aged (8 mo) mice. (B2) Middle-aged mice showed a significant decrease in freezing compared to young (2 mo and 4 mo) mice when exposed to the original context chamber where they had been trained 1 d earlier; (*) P < 0.05.Retention of the auditory CS:US memory was tested 24 h later in a novel context that differed in its location, size, scent, lighting, background noise, and flooring (bedding) compared to the training chamber. Data from three mice (one young, two middle-aged) were excluded because of video malfunction. Following a 150-sec baseline, mice received four presentations of the tone CS in the absence of footshock. Neither baseline freezing (F_(2,31) = 2.6, P = 0.1) nor conditional freezing in response to the tone CS (F_(2,31) = 1.6, P = 0.2) differed between young and middle-aged mice (Fig. 1B1). Thus, retention of the auditory CS following trace fear conditioning was intact in middle-aged compared to young mice. Although deficits in retention of auditory trace fear have been reported in aged mice and rats (Blank et al. 2003; McEchron et al. 2004; Villarreal et al. 2004), the results herein agree with report of intact trace fear memory in middle-aged rats (Moyer Jr and Brown 2006).One hour after this testing, retention of the contextual fear memory was assessed by placing mice in the original context (in the absence of the tone and footshock) and measuring freezing for 10 min. A subtle but significant difference in freezing was observed as a function of age (F_(2,31) = 4.3, P = 0.02; Fig. 1B ​2).2). A student''s post-hoc t-test revealed that mean freezing (collapsed across 10 min) of middle-aged mice was reduced compared to 2-mo (P < 0.05) and 4-mo (P < 0.05) young mice. Contextual fear memory deficits have been similarly reported in aged mice (Fukushima et al. 2008). Studies that failed to observe contextual fear deficits in aged (>18 mo) mice may result from a floor effect because young mice showed weak conditioning to the context (∼30% freezing) (Feiro and Gould 2005; Gould and Feiro 2005) or employment of delay (Corcoran et al. 2002; Feiro and Gould 2005; Gould and Feiro 2005) compared to trace procedures (Moyer Jr and Brown 2006). Although differences in experimental parameters are plausible, heterogeneity in the performance of aged mice may make detection of age-related impairments difficult owing to increased variability.Open in a separate windowFigure 2.Selective deficits on retention of contextual fear in middle-aged weak-learner mice. (A,B) Summary plot and histogram show young mice (100%, n = 6) at 2 mo of age showed robust recall of contextual fear memory (range 75%–99%) with mean and standard deviation (SD) of 91% ± 10%, whereas retention of middle-aged mice (n = 21) varied to a much greater extent (range 21%–95%; M, SD = 74% ± 19%). Distribution of middle-aged mice relative to their mean percent freezing shows two distinct populations. Middle-aged mice with freezing levels less than 3 SD from the mean freezing in young wild-type (WT) mice (61%, dashed line) were characterized as having weak contextual fear memory (weak learners) and those with freezing levels ≥62% as having strong contextual fear memory (learners). (C) Baseline (BL) freezing, expression of post-shock freezing and freezing during retention tests for auditory CS and trace CS memories, were comparable in both weak-learners and learners. (D) Selective deficits in retention of contextual fear memories were observed in middle-aged weak learners as compared to middle-aged learners; (*) P < 0.05.Previous studies in the rat report heterogeneity in spatial water maze and contextual fear conditioning in middle-aged and/or aged rats compared to young animals (Fischer et al. 1992; Wyss et al. 2000; Moyer Jr and Brown 2006). Therefore, we determined if middle-aged impairments of context fear (Fig. 2A) were driven by a subset of impaired mice. The degree of age-related impairment in each middle-aged mouse was determined by comparison to a reference group of young mice tested concurrently (shown in Fig. 1). The behavioral criterion for retention of contextual fear in middle-aged mice was set at 61%, which was 3 standard deviations (SD) below the mean freezing in young mice (mean and SD, 91% ± 10%; Fig. 1). A bimodal distribution of freezing of middle-aged mice was observed (Fig. 2B), where 70% of middle-aged mice performed above criterion and were labeled learners (n = 14), and 30% of middle-aged mice performed below criterion and were labeled as weak learners (n = 6). Comparison on measures of baseline freezing (F_(1,18) = 1.8, P = 0.2) and expression of post-shock freezing (F_(1,18) = 2.1, P = 0.2) revealed no differences between the groups during auditory trace fear training (Fig. 2C). Similarly, no differences in baseline freezing (F_(1,18) = 0.03, P = 0.9) or acquisition/recall of conditioned auditory trace fear (tone, F_(1,18) = 0.08, P = 0.8; trace, F_(1,18) = 2.4, P = 0.1) were observed 24 h later during retention tests. Thus, deficits ascribed to middle-aged weak learners were limited to contextual processing/retention, where middle-aged weak learners responded to the contextual CS with significantly lower levels of freezing compared to middle-aged learners (F_(1,18) = 47, P = 0.001; Fig. 2D). To summarize, we found that onset of cognitive decline in the C56Bl6/SJL mice was first apparent in a subset of middle-aged mice. Middle-aged weak learners showed a mild but specific deficit in hippocampal-dependent contextual learning/memory (spatial learning) but not hippocampal-dependent auditory trace learning/memory (temporal learning), assessed following trace fear conditioning.Given that contextual fear deficits occurred in a subset of middle-age mice, we were able to directly assess age-related alterations in excitability and AHP plasticity in CA1 neurons as they relate to learning abilities (learners vs. weak learners). Within 1 h of cessation of behavioral tests, middle-aged learners and weak learners were decapitated under deep halothane anesthesia and their brains quickly removed and placed into ice-cold artificial cerebral spinal fluid (aCSF): 125 mM NaCl, 25 mM glucose, 25 mM NaHCO₃, 2.5 mM KCl, 1.25 mM NaH₂PO₄, 2 mM CaCl₂, 1 MgCl₂ (pH 7.5, bubbled with 95%O₂/5%CO₂). Naïve mice were removed from their home cage and underwent identical decapitation procedures. Slices (300 μm) of the dorsal hippocampus and adjacent cortex were made using a Leica vibratome. The slices were first incubated for 30 min at 34°C in bubbled aCSF, and held at room temperature in bubbled aCSF for 1–4 h before use. Recording electrodes prepared from thin-walled capillary glass were filled with potassium methylsulfate-based internal solution and had a resistance of 5–6 MΩ.Whole-cell current-clamp recordings were performed on CA1 hippocampal pyramidal neurons of middle-aged learners (n = 36, 14 mice) and weak-learners (n = 15, 6 mice), as well as middle-aged naïve mice (n = 35 cells, 18 mice). Neuronal excitability was compared by measuring the post-burst AHP generated by 25 action potentials at 50 Hz (Fig. 3A), a stimulus shown to reliably evoke an AHP of sizable—but not maximal—amplitude from hippocampal neurons of mice (Ohno et al. 2006b). A significant difference in the peak amplitude of the AHP from learners, weak learners, and naïve mice was observed (F_(2,83) = 5, P < 0.01). Because the peak AHP and sAHP amplitudes did not differ between neurons from weak-learners and naïve mice (Fig. 3B). No differences in membrane resistance (F_(2,83) = 1.6, P = 0.2) or action potential properties, elicited using a brief (2 msec) near threshold current step (pA), were observed (Open in a separate window^aP < 0.05 compared to Weak L.^bP < 0.05 compared to Naïve.^cP < 0.05 compared to Pooled.Open in a separate windowFigure 3.Learning-related AHP plasticity is impaired in middle-aged weak-learner mice. (A) Representative traces showing the sAHP is reduced in neurons from (black) middle-aged learners compared to (blue) weak-learner mice and (gray) naïve mice. (Inset) The medium AHP (mAHP) of neurons from (black) learner mice was decreased compared to (blue) weak-learner mice and (gray) naïve mice. (B) No differences in the AHP from naïve and weak learners were observed; therefore, their data were pooled, and mean AHP was plotted by time on a log scale. (Inset) The mean amplitude of the peak AHP (1 msec) and sAHP (600 msec) was significantly reduced in neurons from learners compared to AHPs from weak learners and naïve labeled control; (*)P < 0.05.The results presented here are important in two respects. First, we demonstrate that the successful acquisition and recall of trace fear conditioning results in a significant reduction in the AHP in CA1 hippocampal neurons from the mouse. Our data are similar to previous reports showing learning-related reductions of the AHP in hippocampal neurons following training on hippocampal-dependent tasks (Disterhoft and Oh 2007) and thus strengthen the case for neuronal excitability change as a general mechanism underlying hippocampal-dependent learning. Second, we demonstrate that the onset of age-related cognitive decline in the C56Bl6/SJL mouse (termed “weak learners”) first manifests as a specific deficit in spatial associative learning in a subset of middle-age mice. These data, combined with a previous report from middle-aged rats (Moyer Jr and Brown 2006), suggest that initiation of age-related hippocampal dysfunction results in specific spatial—as opposed to temporal—deficits in associative learning and memory during middle age. By combining trace fear conditioning with whole-cell patch-clamp recordings in middle-aged mice, we revealed that “early” age-related impairments in spatial associative learning—like those in the aged hippocampus (Tombaugh et al. 2005)—result in part from an impairment of AHP plasticity of hippocampal neurons. Because AHP reductions are poised to facilitate mechanisms crucial for information storage, it is interesting that trace fear conditioning facilitates the long-term potentiation (LTP) of field excitatory postsynaptic potentials in the CA1 region of the rat hippocampus (Song et al. 2008).Generally speaking, both LTP and activation of AHP currents (I_AHP and sI_AHP) are sensitive to changes in intracellular Ca²⁺ (Storm 1990; Sah 1996; Malenka and Nicoll 1999). Thus, dysregulation of Ca²⁺ homeostasis in the hippocampus of middle-aged rats via enhancement of Ca²⁺-induced Ca²⁺ release (CICR) is an important finding (Gant et al. 2006). Age-related enhancement of Ca²⁺-dependent AHPs has been shown to raise the threshold for induction of LTP (Kumar and Foster 2004). These data support our hypothesis that impairments in contextual fear reported herein, as well as deficits in spatial water maze reported in middle-aged rats (Frick et al. 1995; Markowska 1999; Kadish et al. 2009), result from dysfunction of AHP plasticity.Studies in middle-aged mice have important implications for the treatment of “normal” age-associated cognitive decline (AACD), as well as mild cognitive impairment (MCI) (Pepeu 2004). Further studies aim to examine alterations in cholinergic function in our middle-aged mouse model, as the cholinergic agonist carbachol suppressed the AHP in neurons from naïve middle-aged mice (Supplemental Fig. 1). Activation of cholinergic receptors shape neuronal excitability and synaptic throughput (Tai et al. 2006) through multiple Ca²⁺-dependent processes (Gahwiler and Brown 1987; Tai et al. 2006). Restoration of cholinergic function has been shown to rescue deficits on hippocampal-dependent tasks in aged rodent and mouse models of Alzheimer''s disease (AD) (Disterhoft and Oh 2006), as well as in human AD patients (Cummings et al. 1998; Morris et al. 1998; Pettigrew et al. 1998), and therefore is a potential target aimed at the rescue of early age-related cognitive decline.     

Memory for individual items is related to nonreinforced preference change

It is commonly assumed that memories contribute to value-based decisions. Nevertheless, most theories of value-based decision-making do not account for memory influences on choice. Recently, new interest has emerged in the interactions between these two fundamental processes, mainly using reinforcement-based paradigms. Here, we aimed to study the role memory processes play in preference change following the nonreinforced cue-approach training (CAT) paradigm. In CAT, the mere association of cued items with a speeded motor response influences choices. Previous studies with this paradigm showed that a single training session induces a long-lasting effect of enhanced preferences for high-value trained stimuli, that is maintained for several months. We hypothesized that CAT increases memory of trained items, leading to enhanced accessibility of their positive associative memories and in turn to preference changes. In two preregistered experiments, we found evidence that memory is enhanced for trained items and that better memory is correlated with enhanced preferences at the individual item level, both immediately and 1 mo following CAT. Our findings suggest that memory plays a central role in value-based decision-making following CAT, even in the absence of external reinforcements. These findings contribute to new theories relating memory and value-based decision-making and set the groundwork for the implementation of novel nonreinforced behavioral interventions that lead to long-lasting behavioral change.

Value-based decision-making and memory are both extensively studied processes in cognitive psychology and cognitive neuroscience (Fellows 2017). Most theories of value-based decision-making have focused on processes related to the incremental learning of value following external reinforcement, but have not explicitly addressed the role of memory per se. Thus, fundamental questions remain regarding interactions between memory and value-based decisions, which have been gaining attention in recent years.Several recent empirical studies have demonstrated interactions between episodic memory and value-based decision-making. For example, memory for past events has been shown to bias value-based decisions (Duncan and Shohamy 2016), differently for choices of novel versus choices of familiar options (Duncan et al. 2019), and choice behavior and fMRI signals during value-based decision-making were better explained by episodic memory for individual past choices than by a standard reinforcement learning model (Bornstein et al. 2017). Another study has found that during sampling of episodic memories of previous choices, the retrieved context influenced present choices, deviating from the predictions of standard reinforcement learning models (Bornstein and Norman 2017). Other studies have demonstrated that the long time known effect of choices on future preferences is related to memory processes (Chammat et al. 2017; DuBrow et al. 2019; Luettgau et al. 2020). At the neural level, the ventromedial prefrontal cortex (vmPFC) and the hippocampus both have been shown to play a role in memory processes and value-based decisions (Weilbächer and Gluth 2017) and recent studies have been further emphasizing that the hippocampus bridges between past experience and future decisions (Bakkour et al. 2019; Biderman et al. 2020).All these studies, and many others, highlighted the interaction between memory and value-based decision-making involving external reinforcements. However, everyday life involves decisions and associations that are not directly reinforced. Thus, it remains unclear whether memory plays a general role in value-based decision-making even without external reinforcements.To better understand the role of memory processes in shaping preferences independently of external reinforcements, we used a novel behavioral change paradigm, named cue-approach training (CAT). In this paradigm, associating images of items with a neutral cue and a speeded motor response results in a consistent preference enhancement without external reinforcement, which is maintained for months (Schonberg et al. 2014; Bakkour et al. 2018; Salomon et al. 2018, 2019; Botvinik-Nezer et al. 2020). During CAT, images of items are consistently paired with a neutral cue and a speeded motor response (“Go items”), while other items are presented without the cue or the response (“NoGo items”). One training session with several presentations of all items leads to long-lasting preference changes, measured as the likelihood of choosing Go over NoGo items that had similar initial subjective values (Schonberg et al. 2014). Results from over 30 samples with this paradigm have demonstrated a replicable effect on various types of stimuli, including snack food items, fruits and vegetables, unfamiliar faces, fractal art images, and positive affective images (Bakkour et al. 2016, 2017; Veling et al. 2017; Zoltak et al. 2017; Bakkour et al. 2018; Salomon et al. 2018, 2019; Botvinik-Nezer et al. 2020), revealing the potential of the CAT paradigm as an experimental platform for value-based decision-making without external reinforcements (Schonberg and Katz 2020).The underlying mechanisms of the change of preferences following CAT are not yet fully understood (Schonberg et al. 2014; Bakkour et al. 2017; Salomon et al. 2019; Botvinik-Nezer et al. 2020; Schonberg and Katz 2020). The long-lasting nature of the effect, which has been shown to last for up to 6 mo following a single training session (Schonberg et al. 2014; Salomon et al. 2018, 2019; Botvinik-Nezer et al. 2020), raises the hypothesis that memory processes are involved in its maintenance. Furthermore, previous studies have found enhanced memory for Go compared with NoGo items with other types of Go–NoGo tasks (Chiu and Egner 2015a,b; Yebra et al. 2019) and for items for which participants have a sense of agency (Murty et al. 2015). One recent study provided preliminary evidence suggesting that memory is involved in preference change following a similar nonreinforced Go/NoGo training task (Chen et al. 2021).We hypothesized that CAT enhances memory of Go items, which in turn leads to preferring these items over NoGo items. Previous neuroimaging findings with CAT that suggested possible interactions between hippocampal fMRI activity and subsequent preferences 1 mo following CAT, provide additional evidence in support of this hypothesis (Botvinik-Nezer et al. 2020). Therefore, here we set out to test the role memory processes play in the behavioral change of preferences following CAT, in the short and in the long term.We propose an underlying mechanism for the CAT effect, in which preference change following CAT results from a boost in memory encoding of positive Go items, which in itself is a consequence of enhanced perceptual processing of Go items (Schonberg et al. 2014; Botvinik-Nezer et al. 2020). We hypothesize that the enhanced encoding of Go items, as well as the greater perceptual activation in response to them, increases accessibility of attributes and associations of these specific Go items (Anderson 1983; Bhatia 2013). Furthermore, we hypothesized that preference changes, reflected in the binary choice phase, are due to the enhanced accessibility of memory associations of the Go items, which tips the scales in favor of the Go items when the associations are positive.In order to test memory for individual items, in the current work we introduced a memory recognition task following CAT. In two independent preregistered experiments and one pilot experiment, memory was evaluated following a long (16 repetitions) or short (a single exposure) CAT training session, before the probe phase that evaluated post-training preferences. We then tested our predictions that (1) memory will be stronger for Go compared with NoGo items following CAT (more accurate and faster responses in the recognition task) and (2) that memory will be related to choices (better remembered Go items will be chosen over worse remembered NoGo items). Since the link between better memory and enhanced choices is hypothesized to be related to positive associated memories, we tested the relationship between memory and choices separately for choices between low-value and choices between high-value items. These hypotheses were tested both in the short term (immediately or a few days after CAT) and in a 1-mo follow-up.     

LTP in hippocampal area CA1 is induced by burst stimulation over a broad frequency range centered around delta

Long-term potentiation (LTP) is typically studied using either continuous high-frequency stimulation or theta burst stimulation. Previous studies emphasized the physiological relevance of theta frequency; however, synchronized hippocampal activity occurs over a broader frequency range. We therefore tested burst stimulation at intervals from 100 msec to 20 sec (10 Hz to 0.05 Hz). LTP at Schaffer collateral–CA1 synapses was obtained at intervals from 100 msec to 5 sec, with maximal LTP at 350–500 msec (2–3 Hz, delta frequency). In addition, a short-duration potentiation was present over the entire range of burst intervals. We found that N-methyl-d-aspartic acid (NMDA) receptors were more important for LTP induction by burst stimulation, but L-type calcium channels were more important for LTP induction by continuous high-frequency stimulation. NMDA receptors were even more critical for short-duration potentiation than they were for LTP. We also compared repeated burst stimulation with a single primed burst. In contrast to results from repeated burst stimulation, primed burst potentiation was greater when a 200-msec interval (theta frequency) was used, and a 500-msec interval was ineffective. Whole-cell recordings of postsynaptic membrane potential during burst stimulation revealed two factors that may determine the interval dependence of LTP. First, excitatory postsynaptic potentials facilitated across bursts at 500-msec intervals but not 200-msec or 1-sec intervals. Second, synaptic inhibition was suppressed by burst stimulation at intervals between 200 msec and 1 sec. Our data show that CA1 synapses are more broadly tuned for potentiation than previously appreciated.Long-term potentiation (LTP) is used as a model for studying synaptic events during learning and memory (Bliss and Collingridge 1993; Morris 2003; Lynch 2004). At most synapses, LTP is triggered by postsynaptic Ca²⁺ influx through N-methyl-d-aspartic acid (NMDA) glutamate receptors (Collingridge et al. 1983; Harris et al. 1984; Herron et al. 1986) and, under some conditions, through L-type voltage-gated Ca²⁺ channels (Grover and Teyler 1990, 1994; Morgan and Teyler 1999). LTP was discovered in the dentate gyrus (Bliss and Lomo 1973) following several seconds of 10–100 Hz stimulation of the perforant path. Since then, many LTP studies have used similar long, high-frequency stimulation (HFS) protocols, most typically 100 Hz, 1 sec (Bliss and Collingridge 1993). Although effective, HFS does not resemble physiological patterns of activity (Albensi et al. 2007). Patterned stimulation resembling physiological activity, in particular theta burst stimulation, is also effective for LTP induction (Larson et al. 1986; Staubli and Lynch 1987; Capocchi et al. 1992; Nguyen and Kandel 1997). Theta burst stimulation consists of short bursts (4–5 stimuli at 100 Hz) repeated at 5 Hz, which lies within the hippocampal theta frequency range (4–12 Hz) (Bland 1986; Buzsáki 2002). Primed burst stimulation, another form of patterned stimulation, involves delivery of a priming stimulus followed by a single short burst (Larson and Lynch 1986; Rose and Dunwiddie 1986). The temporal requirements for primed burst LTP are quite precise (Diamond et al. 1988; Greenstein et al. 1988; Larson and Lynch 1989): Intervals less than 140 msec or greater than 200 msec are ineffective.The mechanisms underlying theta frequency-dependent LTP have been studied primarily using the primed burst protocol (Larson and Lynch 1986, 1988, 1989; Pacelli et al. 1989; Davies and Collingridge 1996). Activation of GABA_B autoreceptors during the priming stimulus suppresses GABA release during the following burst (Davies et al. 1990; Lambert and Wilson 1994; Olpe et al. 1994), allowing greater postsynaptic depolarization (Larson and Lynch 1986; Pacelli et al. 1989) and more effective NMDA receptor activation (Davies and Collingridge 1996). Consequently, temporal requirements for primed burst potentiation match the time course of GABA_B autoreceptor-mediated suppression of GABA release (Davies et al. 1990; Davies and Collingridge 1993; Mott et al. 1993).Besides theta, hippocampal activity is observed at other frequencies, notably sharp waves (0.01–5 Hz) (Buzsáki 1986, 1989; Suzuki and Smith 1987) and low-frequency oscillations (≤1 Hz) (Wolansky et al. 2006; Moroni et al. 2007). These lower frequencies dominate during slow wave sleep (Buzsáki 1986; Suzuki and Smith 1987; Wolansky et al. 2006; Moroni et al. 2007), and contribute to hippocampal memory processing (Buzsáki 1989; Pennartz et al. 2002). While synchronized population activity over frequencies from <1 Hz to 12 Hz is associated with hippocampal memory function, previous LTP studies have focused on theta. We therefore investigated burst stimulation at frequencies from 0.05 Hz to 10 Hz. We found that CA1 synapses potentiate to some degree over this entire range and that maximal potentiation occurs around delta frequency rather than theta.     

Interactions between prefrontal cortex and cerebellum revealed by trace eyelid conditioning

Eyelid conditioning has proven useful for analysis of learning and computation in the cerebellum. Two variants, delay and trace conditioning, differ only by the relative timing of the training stimuli. Despite the subtlety of this difference, trace eyelid conditioning is prevented by lesions of the cerebellum, hippocampus, or medial prefrontal cortex (mPFC), whereas delay eyelid conditioning is prevented by cerebellar lesions and is largely unaffected by forebrain lesions. Here we test whether these lesion results can be explained by two assertions: (1) Cerebellar learning requires temporal overlap between the mossy fiber inputs activated by the tone conditioned stimulus (CS) and the climbing fiber inputs activated by the reinforcing unconditioned stimulus (US), and therefore (2) trace conditioning requires activity that outlasts the presentation of the CS in a subset of mossy fibers separate from those activated directly by the CS. By use of electrical stimulation of mossy fibers as a CS, we show that cerebellar learning during trace eyelid conditioning requires an input that persists during the stimulus-free trace interval. By use of reversible inactivation experiments, we provide evidence that this input arises from the mPFC and arrives at the cerebellum via a previously unidentified site in the pontine nuclei. In light of previous PFC recordings in various species, we suggest that trace eyelid conditioning involves an interaction between the persistent activity of delay cells in mPFC-a putative mechanism of working memory-and motor learning in the cerebellum.Eyelid conditioning is a form of associative learning that has proven useful for mechanistic studies of learning (Thompson 1986). All variants of eyelid conditioning involve pairing a conditioned stimulus (CS, typically a tone) with a reinforcing unconditioned stimulus (US, mild electrical stimulation near the eye) to promote learned eyelid closure in response to the CS (also known as a conditioned response). Delay eyelid conditioning, where the CS and US overlap in time (Fig. 1A , left), is largely unaffected by forebrain lesions (Solomon et al. 1986; Mauk and Thompson 1987; Kronforst-Collins and Disterhoft 1998; Weible et al. 2000; Powell et al. 2001; McLaughlin et al. 2002) and engages the cerebellum relatively directly (but see Halverson and Freeman 2006). Presentation of the tone and the US are conveyed to the cerebellum via activation of mossy fibers and climbing fibers, respectively (Fig. 1B; Mauk et al. 1986; Steinmetz et al. 1987, 1989; Sears and Steinmetz 1991; Hesslow 1994; Hesslow et al. 1999). In addition, output via a cerebellar deep nucleus is required for the expression of conditioned responses (McCormick and Thompson 1984). This relatively direct mapping of stimuli onto inputs and of output onto behavior makes delay eyelid conditioning a powerful tool for the analysis of cerebellar learning and computation (Mauk and Donegan 1997; Medina and Mauk 2000; Medina et al. 2000, 2002; Hansel et al. 2001; Ohyama et al. 2003).Open in a separate windowFigure 1.The procedures, neural pathways, and putative signals involved in delay and trace eyelid conditioning. (A) Stimulus timing for delay (left) and trace (right) training trials. For delay conditioning, the US overlaps in time with the tone CS. In this and subsequent figures, green is used to indicate the presentation of the CS for delay conditioning. For trace conditioning, the US is presented after CS offset, and “trace interval” refers to the period between CS offset and US onset. For convenience, we used red and maroon regions to represent the CS and trace interval, respectively. Sample conditioned eyelid responses are shown below, for which an upward deflection indicates closure of the eyelid. (B) Schematic representation of the pathways engaged by delay conditioning. The CS and US, respectively, engage mossy fibers and climbing fibers relatively directly, and forebrain input is not required for normal learning. (C) The signals hypothesized to engage the cerebellum during trace conditioning. The activity of mossy fibers directly activated by the tone CS does not significantly outlast the stimulus. Thus, a forebrain structure is thought to provide an input that overlaps in time with the US and is necessary to produce cerebellar learning.Trace eyelid conditioning, where the US is presented after tone offset (Fig. 1A, right), has attracted interest for its potential to reveal the nature of interactions between the forebrain and cerebellum as well as the learning mechanisms within these systems. This potential stems from the sensitivity of trace conditioning not only to lesions of cerebellum but also to lesions of hippocampus, medial prefrontal cortex (mPFC), or mediodorsal thalamic nucleus (Woodruff-Pak et al. 1985; Moyer Jr. et al. 1990; Kronforst-Collins and Disterhoft 1998; Weible et al. 2000; Powell et al. 2001; McLaughlin et al. 2002; Powell and Churchwell 2002; Simon et al. 2005). Given the general inability of forebrain lesions to affect delay conditioning, these results have promoted the general interpretation that the forebrain and cerebellum interact to mediate trace conditioning (Weiss and Disterhoft 1996; Clark and Squire 1998; Clark et al. 2002).Here we test the specific hypotheses that (Fig. 1C) (1) cerebellar learning requires that mossy fiber and climbing fiber inputs overlap in time (or nearly so) and (2) that cerebellar learning in trace conditioning occurs in response to a forebrain-driven mossy fiber input that outlasts the CS to overlap with the US rather than the inputs activated by the tone CS (Clark et al. 2002). The data provide direct support for both assertions and, together with recent anatomical studies (Buchanan et al. 1994; Weible et al. 2007), reveal a pathway between the mPFC and cerebellum that is necessary for the expression of trace eyelid responses. When combined with previous recordings from PFC in primates and rodents (Funahashi et al. 1989; Bodner et al. 1996; Fuster et al. 2000; Narayanan and Laubach 2006), these data support the hypothesis that trace eyelid conditioning is mediated by interactions between working memory-related persistent activity in mPFC and motor learning mechanisms in the cerebellum.     

The NMDA antagonist MK-801 disrupts reconsolidation of a cocaine-associated memory for conditioned place preference but not for self-administration in rats

Recent research suggests that drug-related memories are reactivated after exposure to environmental cues and may undergo reconsolidation, a process that can strengthen memories. Conversely, reconsolidation may be disrupted by certain pharmacological agents such that the drug-associated memory is weakened. Several studies have demonstrated disruption of memory reconsolidation using a drug-induced conditioned place preference (CPP) task, but no studies have explored whether cocaine-associated memories can be similarly disrupted in cocaine self-administering animals after a cocaine priming injection, which powerfully reinstates drug-seeking behavior. Here we used cocaine-induced CPP and cocaine self-administration to investigate whether the N-methyl-D-aspartate receptor antagonist (+)-5methyl-10,11-dihydro-5H-dibenzo[a,d]cyclohepten-5,10-imine maleate (MK-801) given just prior to reactivation sessions would suppress subsequent cocaine-primed reinstatement (disruption of reconsolidation). Systemic injection of MK-801 (0.05 or 0.20 mg/kg administered intraperitoneally) in rats just prior to reactivation of the cocaine-associated memory in the CPP context attenuated subsequent cocaine-primed reinstatement, while no disruption occurred in rats that did not receive reactivation in the CPP context. However, in rats trained to self-administer cocaine, systemic administration of MK-801 just prior to either of two different types of reactivation sessions had no effect on subsequent cocaine-primed reinstatement of lever-pressing behavior. Thus, systemic administration of MK-801 disrupted the reconsolidation of a cocaine-associated memory for CPP but not for self-administration. These findings suggest that cocaine-CPP and self-administration do not use similar neurochemical processes to disrupt reconsolidation or that cocaine-associated memories in self-administering rats do not undergo reconsolidation, as assessed by lever-pressing behavior under cocaine reinstatement conditions.The ability to disrupt previously consolidated memories in a reactivation-dependent manner is thought to be due to the disruption of a memory reconsolidation process. This disruption of reconsolidation has been observed in a wide variety of tasks and species (Nader et al. 2000b; Sara 2000; Alberini 2005; Riccio et al. 2006). Early reconsolidation experiments primarily focused on aversive learning paradigms, with an emphasis on disruption of reconsolidation as a potential treatment for posttraumatic stress disorder (Misanin et al. 1968; Nader et al. 2000a; Debiec and Ledoux 2004; Brunet et al. 2008). Only more recently have investigators demonstrated that appetitive memories also undergo reconsolidation; most, but not all (Yim et al. 2006), studies found a disruption of expression for the drug-associated memory, suggesting the potential to target the reconsolidation process as a treatment for drug addiction (Lee et al. 2005; Miller and Marshall 2005; Milekic et al. 2006; Valjent et al. 2006; Brown et al. 2007; Kelley et al. 2007; Sadler et al. 2007; Fricks-Gleason and Marshall 2008; Milton et al. 2008a, b).Miller and Marshall (2005) showed that reconsolidation of cocaine conditioned place preference (CPP) in the rat could be disrupted by either pre- or post-treatment of a phosphorylation inhibitor of extracellular signal-regulated kinase (1/2) (ERK) in a reactivation-dependent manner. Other studies have shown that protein synthesis inhibitors (Milekic et al. 2006), a matrix metalloproteinase (MMP) inhibitor (Brown et al. 2007), a β-noradrenergic receptor antagonist (Bernardi et al. 2006; Robinson and Franklin 2007a; Fricks-Gleason and Marshall 2008), and an N-methyl-D-aspartate (NMDA) receptor antagonist (Kelley et al. 2007; Sadler et al. 2007) can also disrupt the reconsolidation of drug-associated CPP memories. Studies by Lee and colleagues have shown that Zif268 antisense oligodeoxynucleotide infused into the basolateral amygdala prior to reactivation of memory for a cocaine-associated cue (the conditioned stimulus or CS) disrupts the ability of cocaine-associated cues to establish subsequent acquisition of a new instrumental response (Lee et al. 2005), and the ability of a drug-associated cue to induce relapse under a second-order schedule (Lee et al. 2006a). Thus, cocaine-associated memories appear to undergo reconsolidation in both Pavlovian and operant conditioning paradigms.Relapse to drug-seeking or drug-taking behavior can occur after re-exposure to three types of stimuli: the drug itself, drug-associated contextual and discrete cues, and stress; and all of these may promote relapse in humans (for review, see Epstein et al. 2006). Only a few CPP studies (Valjent et al. 2006; Brown et al. 2007) and no self-administration studies to our knowledge have tested whether the drug-associated memory can be rendered susceptible to disruption by pharmacological agents such that subsequent cocaine-primed reinstatement is suppressed. This drug-primed effect is observed in humans, producing relapse (Ludwig et al. 1974; Jaffe et al. 1989), and in rats, producing robust reinstatement of drug-seeking behavior in both CPP and self-administration tasks (McFarland and Ettenberg 1997; McFarland and Kalivas 2001; Sanchez and Sorg 2001; Kalivas and McFarland 2003). The development of a treatment strategy that makes use of the reconsolidation process will ultimately need to be powerful enough to diminish drug-seeking behavior in the presence of sizable doses of the drug itself. Therefore, the primary goal of this study was to determine whether drug-primed reinstatement could be suppressed in rats that have the memory reactivated in the presence of a pharmacological agent in cocaine self-administering rats. Since we previously have demonstrated the ability to disrupt cocaine-primed reinstatement only in animals in which the memory was reactivated using cocaine-induced CPP, we also tested the extent to which the same parameters used to disrupt reconsolidation in a cocaine-induced CPP task would disrupt reconsolidation in a cocaine self-administration task under conditions of drug-induced reinstatement.To examine this question, we chose the noncompetitive NMDA receptor antagonist (+)-5-methyl-10,11-dihydro-5H-dibenzo[a,d]cyclohepten-5,10-imine maleate (MK-801). MK-801 has been shown to disrupt reconsolidation of spatial tasks (Przybyslawski and Sara 1997), fear tasks (Lee et al. 2006b), amphetamine-induced CPP (Sadler et al. 2007), cocaine-induced CPP (Kelley et al. 2007), and sucrose self-administration (Lee and Everitt 2008). Importantly, the two studies examining CPP using MK-801 did not explore whether MK-801 suppressed drug-seeking behavior in a manner that was dependent on whether the memory was reactivated, leaving open the possibility that it was not a reconsolidation process that was disrupted by MK-801.Here we demonstrate that MK-801 injected prior to cocaine-primed reinstatement of CPP disrupted subsequent cocaine-primed reinstatement of CPP, and this disruption was dependent on CPP contextual reactivation since injection of MK-801 and cocaine in the home cage did not disrupt subsequent cocaine-primed reinstatement of CPP. However, drug-seeking behavior in animals trained for cocaine self-administration was not disrupted when rats were reactivated under the same parameters that disrupted cocaine-induced CPP or when rats were given a reactivation session identical to their self-administration sessions. We thus demonstrate for the first time that memories associated with cocaine-induced CPP and cocaine self-administration are not similarly susceptible to disruption by MK-801.     

The effect of zolpidem on targeted memory reactivation during sleep

According to the active system consolidation theory, memory consolidation during sleep relies on the reactivation of newly encoded memory representations. This reactivation is orchestrated by the interplay of sleep slow oscillations, spindles, and theta, which are in turn modulated by certain neurotransmitters like GABA to enable long-lasting plastic changes in the memory store. Here we asked whether the GABAergic system and associated changes in sleep oscillations are functionally related to memory reactivation during sleep. We administered the GABA_A agonist zolpidem (10 mg) in a double-blind placebo-controlled study. To specifically focus on the effects on memory reactivation during sleep, we experimentally induced such reactivations by targeted memory reactivation (TMR) with learning-associated reminder cues presented during post-learning slow-wave sleep (SWS). Zolpidem significantly enhanced memory performance with TMR during sleep compared with placebo. Zolpidem also increased the coupling of fast spindles and theta to slow oscillations, although overall the power of slow spindles and theta was reduced compared with placebo. In an uncorrected exploratory analysis, memory performance was associated with slow spindle responses to TMR in the zolpidem condition, whereas it was associated with fast spindle responses in placebo. These findings provide tentative first evidence that GABAergic activity may be functionally implicated in memory reactivation processes during sleep, possibly via its effects on slow oscillations, spindles and theta as well as their interplay.

Sleep supports the consolidation of newly acquired memories (Mednick et al. 2011; Klinzing et al. 2019). According to the active system consolidation theory, new memories and their associated neuronal activation patterns become spontaneously reactivated (replayed) following learning in the sleeping brain (Wilson and McNaughton 1994; Diekelmann and Born 2010). These reactivations allow for the redistribution and integration of the memory representations from hippocampal to neocortical sites for long-term storage (Rasch and Born 2007; Klinzing et al. 2019). Memory reactivation during sleep has been proposed to rely on the synchronized interplay of electrophysiological oscillations characteristic of non–rapid eye movement (NREM) sleep, mainly neocortical slow oscillations (SOs, <1 Hz), thalamocortical spindles (9–15 Hz), and hippocampal ripples (80–200 Hz) (Mölle et al. 2009; Staresina et al. 2015; Helfrich et al. 2018; Ngo et al. 2020). Particularly, sleep spindles and their intricate phase coupling to SO have been suggested to be mechanistically involved in memory consolidation processes during sleep (Ulrich 2016; Antony et al. 2019). It has been proposed that memories become reinstated by spindle events, specifically during the up-state of slow oscillations, allowing for the flow of information between different brain sites as well as the induction of lasting structural and functional plastic changes in the learning-associated neuronal networks (Rosanova and Ulrich 2005; Peyrache and Seibt 2020). In addition to sleep spindles, neocortical and hippocampal theta activity (4–8 Hz) is also phase-locked to SO during NREM sleep (Gonzalez et al. 2018; Cox et al. 2019; Krugliakova et al. 2020), and this coupling has been related to memory consolidation during sleep (Schreiner et al. 2018).A number of neuromodulators seem to be involved in the generation of sleep spindles, SO and associated memory processing, most notably GABA (γ-aminobutyric acid), which is the major inhibitory neurotransmitter (Lancel 1999; Ulrich et al. 2018). Sleep spindles and sleep-dependent memory processing can be boosted by targeting the GABAergic system pharmacologically (Mednick et al. 2013). Zolpidem is one of the most frequently used drugs in this regard, binding to GABA_A receptors at the same location as benzodiazepines, thereby acting as a GABA_A receptor agonist (Lemmer 2007). Zolpidem increases the time spent in slow-wave sleep (SWS) and reduces the amount of rapid eye movement (REM) sleep (Kanno et al. 2000; Uchimura et al. 2006; Zhang et al. 2020). Zolpidem also increases the density and power of sleep spindles (Dijk et al. 2010; Lundahl et al. 2012; Mednick et al. 2013; Niknazar et al. 2015; Zhang et al. 2020) as well as the coupling of spindles to SO (Niknazar et al. 2015; Zhang et al. 2020), and it was further found to enhance declarative memory consolidation during sleep, with postsleep performance improvements being associated with higher spindle density and spindle power as well as with SO–spindle coupling (Kaestner et al. 2013; Mednick et al. 2013; Zhang et al. 2020).However, it remains unclear whether the changes in sleep stages, sleep spindles, and SO–spindle coupling after pharmacological manipulation with zolpidem are functionally related to the mechanisms underlying sleep-dependent memory consolidation such as memory reactivation. Over the last few years, targeted memory reactivation (TMR) has been increasingly applied to manipulate memory reactivation during sleep experimentally by presenting learning-associated reminder cues like odors or sounds (Oudiette and Paller 2013; Hu et al. 2020; Klinzing and Diekelmann 2020). TMR biases sleep-related neuronal replay events toward the reactivated memory contents (Lewis and Bendor 2019) and enhances subsequent recall performance (Rudoy et al. 2009; Diekelmann et al. 2011; Schreiner et al. 2015; Cairney et al. 2018). Although a few studies observed modulations of SOs (Rihm et al. 2014), sleep spindles (Cox et al. 2014), and SO–spindle coupling (Bar et al. 2020) with TMR during sleep, studies on the role of specific neurotransmitters and particularly on the role of GABAergic neurotransmission and associated changes in sleep oscillations for targeted memory reactivation are entirely lacking. One previous study tested the effect of pharmacologically increased GABAergic activity by administering the benzodiazepine clonazepam after cued reactivation of a declarative memory during wakefulness (Rodríguez et al. 2013). Clonazepam increased memory performance when it was administered after reactivation with an incomplete reminder cue, suggesting that increasing GABAergic neurotransmission may enhance the restabilization of reactivated declarative memories in humans during wakefulness.In the present study, we tested the effect of modulating GABAergic activity with zolpidem on targeted memory reactivation during sleep and associated changes in sleep spindles as well as SO–spindle and SO–theta coupling. We hypothesized that zolpidem enhances the beneficial effects of targeted memory reactivation on memory performance and that this enhancement is associated with increases in spindle density, spindle power, SO–spindle coupling, and possibly SO–theta coupling, and the amount of SWS. Participants were trained on a memory task including 30 sound–word associations in the evening (Forcato et al. 2020) and received an oral dose of 10 mg zolpidem (n = 11) or placebo (n = 11) after training before a full night of sleep in the sleep lab (Fig. 1). During the night, incomplete reminder cues (sounds + first syllable of the associated words) were played again via in-ear headphones during SWS. The next morning, participants were trained on an interference memory task to probe the stability of the original memory, which was tested 30 min later.Open in a separate windowFigure 1.Experimental design and memory task. (A) All subjects took part in a training session at ∼22.30, were administered with placebo (n = 11) or 10 mg of zolpidem (n = 11) before going to bed at 23:00, and received targeted memory reactivation during the first SWS period. After ∼8 h of sleep, in the morning, subjects learned an interference task and were tested on the original memory task in a testing session 30 min after the interference task. (B) Training: First, subjects were presented with 30 sound–word associations for learning. For each association, the sound was presented first for 2900 msec. The sound then continued accompanied by the word written on the screen and spoken aloud for 1500 msec. After a 4000-msec break, the next association was presented in the same way. After all associations were presented once, participants completed an immediate cued recall test. For each association, the sound was presented for 2900 msec. The sound then continued accompanied by the first syllable of the associated word for 1500 msec. Participants were then given 5000 msec to say the complete word aloud (sound continued during the entire period). Independently of their response, the correct answer was then presented on the screen and via headphones for 1500 msec. Reactivation: Each sound was first presented alone for an average of 2900 msec; the sound then continued accompanied by the first syllable of each word for another 1500 msec. After a 7000-msec break, the next sound–syllable pair was presented until all 30 pairs had been presented once. Testing: Each sound was presented for 500 msec and then the sound continued and subjects had 5000 msec to say the associated word aloud. After a break of 4000 msec, the procedure continued for the rest of the 30 associations. Adapted from Forcato et al. (2020).     

Stimulation of the lateral geniculate,superior colliculus,or visual cortex is sufficient for eyeblink conditioning in rats

The role of the cerebellum in eyeblink conditioning is well established. Less work has been done to identify the necessary conditioned stimulus (CS) pathways that project sensory information to the cerebellum. A possible visual CS pathway has been hypothesized that consists of parallel inputs to the pontine nuclei from the lateral geniculate nucleus (LGN), superior colliculus (SC), pretectal nuclei, and visual cortex (VCTX) as reported by Koutalidis and colleagues in an earlier paper. The following experiments examined whether electrical stimulation of neural structures in the putative visual CS pathway can serve as a sufficient CS for eyeblink conditioning in rats. Unilateral stimulation of the ventral LGN (Experiment 1), SC (Experiment 2), or VCTX (Experiment 3) was used as a CS paired with a periorbital shock unconditioned stimulus. Stimulation was delivered to the hemisphere contralateral to the conditioned eye. Rats in all experiments were given five 100-trial sessions of paired or unpaired eyeblink conditioning with the stimulation CS followed by three paired sessions with a light CS. Stimulation of each visual area when paired with the unconditioned stimulus supported acquisition of eyeblink conditioned responses (CRs) and substantial savings when switched to a light CS. The results provide evidence for a unilateral parallel visual CS pathway for eyeblink conditioning that includes the LGN, SC, and VCTX inputs to the pontine nuclei.Pavlovian eyeblink (eyelid closure and nictitating membrane movement) conditioning is established by pairing a conditioned stimulus (CS), usually a tone or light, with an unconditioned stimulus (US) that elicits the eyeblink reflex. The eyeblink conditioned response (CR) emerges over the course of paired training, occurs during the CS, and precedes the US (Gormezano et al. 1962; Schneiderman et al. 1962). Neurobiological investigations of Pavlovian eyeblink conditioning have primarily focused on the cerebellum, which is the site of memory formation and storage (Thompson 2005). The anterior interpositus nucleus is necessary for acquisition and retention of the eyeblink CR (Lavond et al. 1985; Krupa and Thompson 1997; Freeman Jr. et al. 2005; Thompson 2005; Ohyama et al. 2006). Lobule HVI and the anterior lobe of the cerebellar cortex (lobules I–V) contribute to acquisition, retention, and timing of the CR (McCormick and Thompson 1984; Perrett et al. 1993; Perrett and Mauk 1995; Attwell et al. 1999, 2001; Medina et al. 2000; Nolan and Freeman Jr. 2005; Nolan and Freeman 2006). The brainstem nuclei that comprise the proximal ends of the CS and US input pathways to the cerebellum have also been identified.The pontine nuclei (PN) and inferior olive (IO) receive CS and US information, respectively, and are the primary sensory relays into the interpositus nucleus and cerebellar cortex (Thompson 2005). Conditioned stimulus information converges in the PN, which receives projections from lower brainstem, thalamus, and cerebral cortex (Glickstein et al. 1980; Brodal 1981; Schmahmann and Pandya 1989; Knowlton et al. 1993; Campolattaro et al. 2007). The lateral pontine nuclei (LPN) are the sources of auditory CS information projected into the cerebellum. Lesions of the LPN block CR retention to a tone CS, but have no effect on CRs to a light CS (Steinmetz et al. 1987). Thus, CS inputs from different sensory modalities may be segregated at the level of the PN. Neurons in the PN project CS information into the contralateral cerebellum via mossy fibers in the middle cerebellar peduncle that synapse primarily on granule cells in the cerebellar cortex and on neurons in the deep nuclei (Bloedel and Courville 1981; Brodal 1981; Steinmetz and Sengelaub 1992). Stimulation of the PN acts as a supernormal CS supporting faster CR acquisition than conditioning with peripheral stimuli (Steinmetz et al. 1986, 1989; Rosen et al. 1989; Steinmetz 1990; Tracy et al. 1998; Freeman Jr. and Rabinak 2004). The primary focus of these experiments was to investigate the most proximal components of the CS pathway in eyeblink conditioning. There has been less emphasis on identifying the critical CS pathways that project information to the PN.Recent studies using lesions, inactivation, stimulation, and neural tract tracing have provided evidence that the auditory CS pathway that is necessary for acquisition and retention of eyeblink conditioning is comprised of converging inputs to the medial auditory thalamic nuclei (MATN), and a direct ipsilateral projection from the MATN to the PN (Halverson and Freeman 2006; Campolattaro et al. 2007; Freeman et al. 2007; Halverson et al. 2008). Unilateral lesions of the MATN, contralateral to the conditioned eye, block acquisition of eyeblink CRs to a tone CS but have no effect on conditioning with a light CS (Halverson and Freeman 2006). Inactivation of the MATN with muscimol blocks acquisition and retention of CRs to an auditory CS, and decreases metabolic activity in the PN (Halverson et al. 2008). The MATN has a direct projection to the PN and stimulation of the MATN supports rapid CR acquisition (Campolattaro et al. 2007). Our current model of the auditory CS pathway consists of converging inputs to the MATN, and direct unilateral thalamic input to the PN (Halverson et al. 2008).Less work has been done to identify the visual CS pathway necessary for eyeblink conditioning. A possible parallel visual CS pathway has been hypothesized, which includes parallel inputs to different areas of the PN from the lateral geniculate nucleus (LGN), superior colliculus (SC), visual cortex (VCTX), and pretectal nuclei (Koutalidis et al. 1988). In the Koutalidis et al. study, lesions of the LGN, SC, VCTX, or pretectal nuclei alone had only a partial effect on CR acquisition with a light CS. Lesions of any two of these structures together produced a more severe impairment on acquisition and combined lesions of all of these areas completely blocked CR acquisition to a light CS (Koutalidis et al. 1988). Each visual area investigated in the Koutalidis et al. study has a direct projection to the PN that could be important for eyeblink conditioning. The ventral LGN projects to the medial, and to a lesser extent, the lateral PN (Graybiel 1974; Wells et al. 1989). The superficial, intermediate, and deep layers of SC send projections to both the dorsomedial and dorsolateral PN (Redgrave et al. 1987; Wells et al. 1989). The VCTX has a direct projection to the rostral and lateral portions of the PN (Glickstein et al. 1972; Baker et al. 1976; Mower et al. 1980; Wells et al. 1989). The pretectal nuclei also have a direct projection to both the medial and lateral PN (Weber and Harting 1980; Wells et al. 1989). However, stimulation of the anterior pretectal nucleus is not an effective CS for eyeblink conditioning (Campolattaro et al. 2007). The failure to establish conditioning with stimulation of the anterior pretectal nucleus as a CS suggests that there may be differences in the efficacy of the various visual inputs to the PN for cerebellar learning. The following experiments investigated the sufficiency of stimulation of the LGN, SC, or primary VCTX as a CS for eyeblink conditioning in rats.     

Beta-adrenergic receptor activation during distinct patterns of stimulation critically modulates the PKA-dependence of LTP in the mouse hippocampus

Activation of β-adrenergic receptors (β-ARs) enhances hippocampal memory consolidation and long-term potentiation (LTP), a likely mechanism for memory storage. One signaling pathway linked to β-AR activation is the cAMP-PKA pathway. PKA is critical for the consolidation of hippocampal long-term memory and for the expression of some forms of long-lasting hippocampal LTP. How does β-AR activation affect the PKA-dependence, and persistence, of LTP elicited by distinct stimulation frequencies? Here, we use in vitro electrophysiology to show that patterns of stimulation determine the temporal phase of LTP affected by β-AR activation. In addition, only specific patterns of stimulation recruit PKA-dependent LTP following β-AR activation. Impairments of PKA-dependent LTP maintenance generated by pharmacologic or genetic deficiency of PKA activity are also abolished by concurrent activation of β-ARs. Taken together, our data show that, depending on patterns of synaptic stimulation, activation of β-ARs can gate the PKA-dependence and persistence of synaptic plasticity. We suggest that this may allow neuromodulatory receptors to fine-tune neural information processing to meet the demands imposed by numerous synaptic activity profiles. This is a form of “metaplasticity” that could control the efficacy of consolidation of hippocampal long-term memories.The hippocampus importantly contributes to memory function in the mammalian brain (Zola-Morgan et al. 1986; Eichenbaum et al. 1990; Otto and Eichenbaum 1992; Phillips and LeDoux 1992; Remondes and Schuman 2004). It has reciprocal connections with numerous cortical areas, including those responsible for high-level integration of spatial and contextual data from the external environment (Lavenex and Amaral 2000). As such, the hippocampus is well positioned to receive and survey a broad range of information and select behaviorally salient data for long-term storage. Activity-dependent enhancement of hippocampal synaptic strength can store information carried in patterns of afferent neural activity (Bliss and Collingridge 1993; Moser et al. 1998; Nathe and Frank 2003; Whitlock et al. 2006). Substantial evidence suggests that long-term potentiation (LTP) of synaptic strength plays important roles in the formation of long-term memory (LTM) (Doyere and Laroche 1992; Bourtchuladze et al. 1994; Abel and Lattal 2001; Genoux et al. 2002). As such, mechanistic studies of LTP have shed valuable light on how the mammalian brain stores new information.The hippocampus receives dense noradrenergic projections from the locus coeruleus, a brain structure that can influence many vital brain functions, including attention, sleep, arousal, mood regulation, learning, and memory (Berridge and Waterhouse 2003). Both α- and β-adrenergic receptor subtypes are present on hippocampal neurons (Morrison and Foote 1986; Berridge and Waterhouse 2003), and noradrenaline (NA) acts on hippocampal β-adrenergic receptors (β-ARs) to facilitate the retention and recall of memory (Izquierdo et al. 1998; Ji et al. 2003; Murchison et al. 2004). In humans, stimulation of the noradrenergic neuromodulatory system enhances memory for emotional stimuli, and inhibition of β-ARs prevents this memory enhancement (Cahill et al. 1994; van Stegeren et al. 1998; O’Carroll et al. 1999).Consistent with the notion that selective enhancement of LTM may occur following β-AR activation, stimulation of β-ARs can also facilitate the persistence of LTP. In areas CA3 and CA1, β-AR activation facilitates the induction of long-lasting LTP when paired with certain patterns of electrical stimulation (Huang and Kandel 1996; Gelinas and Nguyen 2005). However, the mechanisms by which different patterns of stimulation control synaptic responsiveness to β-AR activation are unclear.β-ARs couple to guanine-nucleotide-binding regulatory Gs proteins to stimulate adenylyl cyclase activity and increase intracellular cAMP (Seeds and Gilman 1971; Maguire et al. 1977). A main target of cAMP signaling is activation of cAMP-dependent protein kinase (PKA), a kinase that is required for some forms of long-lasting LTP and for consolidation of hippocampal LTM (Frey et al. 1993; Abel et al. 1997; Nguyen and Woo 2003). Interestingly, the PKA-dependence of hippocampal LTP displays plasticity: Specific temporal patterns of synaptic stimulation, such as repeated and temporally spaced 100-Hz stimulation, elicit LTP that requires PKA for its expression (Woo et al. 2003). Also, spatial “enrichment” can increase the PKA-dependence of LTP in mice, and this is correlated with improved hippocampal memory function (Duffy et al. 2001). However, it is unclear whether activation of β-ARs can critically gate the PKA-dependence of LTP. In this study, we examine the effects of β-AR activation on LTP generated by various patterns of afferent stimulation in area CA1 of the hippocampus, and we determine the role of PKA in these β-AR-modulated forms of LTP.